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: "surprising negative results for generative adversarial tree search"
  Abstract: While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics. One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS), a model-based method, with deep-Q networks (DQNs), a model-free method. MCTS requires generating rollouts, which is computationally expensive. In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment. Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN-based dynamics model and a reward predictor. GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states. Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions. However, GATS fails to outperform DQNs. Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds. We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.

Connected through hyperedge HE-1 (induced by source paper "structured agents for physical construction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "unity a general platform for intelligent agents"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "mastering atari go chess and shogi by planning with a learned model"):
  with members:
    - "off policy actor critic with shared experience replay"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial imagination for sample efficient deep reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "distributed distributional deterministic policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "a distributional perspective on reinforcement learning"
    - "recurrent environment simulators"
    - "improved training of wasserstein gans"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "using monte carlo tree search as a demonstrator within asynchronous deep rl"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning montezuma s revenge from a single demonstration"
    - "pommerman a multi agent playground"
    - "deep reinforcement learning a brief survey"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"

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.LG(Machine Learning)