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: "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
  Abstract: We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of deep Q-learning network variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space.

Connected through hyperedge HE-1 (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"
    - "recurrent world models facilitate policy evolution"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "reinforcing classical planning for adversary driving scenarios"):
  with members:
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "socially aware motion planning with deep reinforcement learning"
    - "long term planning by short term prediction"
    - "end to end learning for self driving cars"
    - "the option critic architecture"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "single step options for adversary driving"):
  with members:
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "socially aware motion planning with deep reinforcement learning"
    - "long term planning by short term prediction"
    - "end to end learning for self driving cars"
    - "the option critic architecture"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "giraffe using deep reinforcement learning to play chess"

Connected through hyperedge HE-4 (induced by source paper "spatial positioning token sptoken for smart mobility"):
  with members:
    - "iota based directed acyclic graphs without orphans"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "deconvolving feedback loops in recommender systems"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

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.NE(Neural and Evolutionary Computing)