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: "extreme state aggregation beyond mdps"
  Abstract: We consider a Reinforcement Learning setup where an agent interacts with an environment in observation-reward-action cycles without any (esp.\ MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs.

Connected through hyperedge HE-1 (induced by source paper "information theoretic considerations in batch reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "on oracle efficient pac rl with rich observations"
    - "deep reinforcement learning and the deadly triad"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "scalable bilinear pi learning using state and action features"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "near optimal behavior via approximate state abstraction"

Connected through hyperedge HE-2 (induced by source paper "approximate exploration through state abstraction"):
  with members:
    - "exploration by random network distillation"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "parameter space noise for exploration"
    - "exploration exploitation in mdps with options"
    - "near optimal behavior via approximate state abstraction"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "near optimal behavior via approximate state abstraction"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "metrics for finite markov decision processes"
    - "methods for computing state similarity in markov decision processes"
    - "graphstream a tool for bridging the gap between complex systems and dynamic graphs"
    - "hierarchical reinforcement learning with the maxq value function decomposition"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "self modification of policy and utility function in rational agents"):
  with members:
    - "thompson sampling is asymptotically optimal in general environments"
    - "avoiding wireheading with value reinforcement learning"
    - "bad universal priors and notions of optimality"
    - "model based utility functions"
    - "universal intelligence a definition of machine intelligence"

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)