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: "a fast integrated planning and control framework for autonomous driving via imitation learning"
  Abstract: For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "efficient motion planning for automated lane change based on imitation learning and mixed integer optimization"):
  with members:
    - "a data driven method of optimizing feedforward compensator for autonomous vehicle"
    - "self driving cars a survey"
    - "learning a lattice planner control set for autonomous vehicles"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-2 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "human motion trajectory prediction a survey"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "an lstm network for highway trajectory prediction"
    - "deep imitative models for flexible inference planning and control"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"

Connected through hyperedge HE-3 (induced by source paper "design space of behaviour planning for autonomous driving"):
  with members:
    - "learning on road visual control for self driving vehicles with auxiliary tasks"
    - "world models"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "environment independent task specifications via gltl"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "learning sampling distributions for robot motion planning"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-4 (induced by source paper "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"):
  with members:
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "deep object centric policies for autonomous driving"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-5 (induced by source paper "game theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation"):
  with members:
    - "a game theoretic approach to decision making for multiple vehicles at roundabout"
    - "game theoretic modeling of multi vehicle interactions at uncontrolled intersections"
    - "decision making for autonomous vehicles at unsignalized intersection in presence of malicious vehicles"
    - "using control synthesis to generate corner cases a case study on autonomous driving"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "adaptive game theoretic decision making for autonomous vehicle control at roundabouts"
    - "carla an open urban driving simulator"
    - "belief state planning for autonomously navigating urban intersections"

Connected through hyperedge HE-6 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "plato policy learning using adaptive trajectory optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "differentiable mpc for end to end planning and control"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "mpc inspired neural network policies for sequential decision making"
    - "zero shot visual imitation"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "combining model based and model free updates for trajectory centric reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep imitative models for flexible inference planning and control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "human motion trajectory prediction a survey"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "differentiable mpc for end to end planning and control"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "rethinking self driving multi task knowledge for better generalization and accident explanation ability"

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)