human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "flow a modular learning framework for autonomy in traffic"
  Abstract: The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, due to numerous technical, political, and human factors challenges, new methodologies are needed to design vehicles and transportation systems for these positive outcomes. This article tackles important technical challenges arising from the partial adoption of autonomy (hence termed mixed autonomy, to involve both AVs and human-driven vehicles): partial control, partial observation, complex multi-vehicle interactions, and the sheer variety of traffic settings represented by real-world networks. To enable the study of the full diversity of traffic settings, we first propose to decompose traffic control tasks into modules, which may be configured and composed to create new control tasks of interest. These modules include salient aspects of traffic control tasks: networks, actors, control laws, metrics, initialization, and additional dynamics. Second, we study the potential of model-free deep Reinforcement Learning (RL) methods to address the complexity of traffic dynamics. The resulting modular learning framework is called Flow. Using Flow, we create and study a variety of mixed-autonomy settings, including single-lane, multi-lane, and intersection traffic. In all cases, the learned control law exceeds human driving performance (measured by system-level velocity) by at least 40% with only 5-10% adoption of AVs. In the case of partially-observed single-lane traffic, we show that a low-parameter neural network control law can eliminate commonly observed stop-and-go traffic. In particular, the control laws surpass all known model-based controllers, achieving near-optimal performance across a wide spectrum of vehicle densities (even with a memoryless control law) and generalizing to out-of-distribution vehicle densities.

Connected through hyperedge HE-1 (induced by source paper "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems"):
  with members:
    - "dissipation of stop and go waves via control of autonomous vehicles field experiments"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"
    - "sequential attacks on agents for long term adversarial goals"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

Connected through hyperedge HE-2 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-4 (induced by source paper "attentional policies for cross context multi agent reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "actor attention critic for multi agent reinforcement learning"
    - "self attention with relative position representations"
    - "relational inductive biases deep learning and graph networks"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "a review on cooperative adaptive cruise control cacc systems architectures controls and applications"

Connected through hyperedge HE-5 (induced by source paper "multi agent deep reinforcement learning with adaptive policies"):
  with members:
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "model based reinforcement learning for predictions and control for limit order books"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "world models"
    - "learning montezuma s revenge from a single demonstration"
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-7 (induced by source paper "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"):
  with members:
    - "demonstration of a time efficient mobility system using a scaled smart city"
    - "dissipation of stop and go waves via control of autonomous vehicles field experiments"
    - "control of connected and automated vehicles state of the art and future challenges"
    - "simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"):
  with members:
    - "a deep reinforcement learning network for traffic light cycle control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "cm3 cooperative multi goal multi stage multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "intelligent traffic light control using distributed multi agent q learning"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "value decomposition networks for cooperative multi agent learning"
    - "using a deep reinforcement learning agent for traffic signal control"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.AI(Artificial Intelligence)