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: "combining neural networks and tree search for task and motion planning in challenging environments"
  Abstract: We consider task and motion planning in complex dynamic environments for problems expressed in terms of a set of Linear Temporal Logic (LTL) constraints, and a reward function. We propose a methodology based on reinforcement learning that employs deep neural networks to learn low-level control policies as well as task-level option policies. A major challenge in this setting, both for neural network approaches and classical planning, is the need to explore future worlds of a complex and interactive environment. To this end, we integrate Monte Carlo Tree Search with hierarchical neural net control policies trained on expressive LTL specifications. This paper investigates the ability of neural networks to learn both LTL constraints and control policies in order to generate task plans in complex environments. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying given rules of the road.

Connected through hyperedge HE-1 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "limits of end to end learning"
    - "on a formal model of safe and scalable self driving cars"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "brain inspired cognitive model with attention for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "task motion planning for safe and efficient urban driving"):
  with members:
    - "interpretable end to end urban autonomous driving with latent deep reinforcement learning"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "learning from interventions using hierarchical policies for safe learning"
    - "augmenting knowledge through statistical goal oriented human robot dialog"
    - "learning to guide task and motion planning using score space representation"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "path planning in dynamic environments using generative rnns and monte carlo tree search"):
  with members:
    - "human motion trajectory prediction a survey"
    - "predicting responses to a robot s future motion using generative recurrent neural networks"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-4 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search"):
  with members:
    - "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "learning to predict ego vehicle poses for sampling based nonholonomic motion planning"
    - "decentralized cooperative planning for automated vehicles with continuous monte carlo tree search"
    - "decentralized cooperative planning for automated vehicles with hierarchical monte carlo tree search"
    - "learning sampling distributions for robot motion planning"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-5 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-6 (induced by source paper "conditional driving from natural language instructions"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "prospection interpretable plans from language by predicting the future"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "driving policy transfer via modularity and abstraction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

Connected through hyperedge HE-7 (induced by source paper "efficient bimanual manipulation using learned task schemas"):
  with members:
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "active model learning and diverse action sampling for task and motion planning"
    - "learning to guide task and motion planning using score space representation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "meta learning shared hierarchies"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-8 (induced by source paper "social attention for autonomous decision making in dense traffic"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "deep imitative models for flexible inference planning and control"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "learning to drive in a day"
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "recurrent world models facilitate policy evolution"

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.RO(Robotics)