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: "thinking fast and slow with deep learning and tree search"
  Abstract: Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex, the previous state-of-the-art Hex player.

Connected through hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "distilling policy distillation"
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "adversarial exploitation of policy imitation"
    - "wasserstein robust reinforcement learning"
    - "rl based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies"
    - "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"

Connected through hyperedge HE-2 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "learning sampling distributions for robot motion planning"

Connected through hyperedge HE-4 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "meta learning of sequential strategies"
    - "grunge a grand unified atp challenge"
    - "analysing mathematical reasoning abilities of neural models"
    - "graph representations for higher order logic and theorem proving"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"

Connected through hyperedge HE-5 (induced by source paper "sample efficient model free reinforcement learning with off policy critics"):
  with members:
    - "exploration by random network distillation"
    - "dual policy iteration"
    - "information directed exploration for deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rudder return decomposition for delayed rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "combining q learning and search with amortized value estimates"):
  with members:
    - "action guidance with mcts for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "policy gradient search online planning and expert iteration without search trees"
    - "benchmarking model based reinforcement learning"
    - "an investigation of model free planning"
    - "unsupervised visuomotor control through distributional planning networks"
    - "structured agents for physical construction"
    - "universal planning networks"

Connected through hyperedge HE-7 (induced by source paper "information theoretic model predictive q learning"):
  with members:
    - "an online learning approach to model predictive control"
    - "deepmind control suite"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "model ensemble trust region policy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "model based active exploration"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-8 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "go explore a new approach for hard exploration problems"
    - "continual lifelong learning with neural networks a review"
    - "rinascimento optimising statistical forward planning agents for playing splendor"
    - "a local approach to forward model learning results on the game of life game"
    - "deep reinforcement learning for general video game ai"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"

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)