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: "backplay man muss immer umkehren"
  Abstract: Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.

Connected through hyperedge HE-1 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "competitive experience replay"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "forward backward reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "interactive fiction games a colossal adventure"):
  with members:
    - "nail a general interactive fiction agent"
    - "go explore a new approach for hard exploration problems"
    - "interactive language learning by question answering"
    - "learning to speak and act in a fantasy text adventure game"
    - "comprehensible context driven text game playing"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-5 (induced by source paper "safer deep rl with shallow mcts a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "real time tree search with pessimistic scenarios"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "monte carlo tree search for simulation based strategy analysis"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-6 (induced by source paper "generative exploration and exploitation"):
  with members:
    - "exploration by random network distillation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "deep reinforcement learning for general video game ai"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"
    - "lenient multi agent deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "real time tree search with pessimistic scenarios"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "continual match based training in pommerman technical report"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "pommerman a multi agent playground"

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)