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: "on polynomial time pac reinforcement learning with rich observations"
  Abstract: We study the computational tractability of provably sample-efficient (PAC) reinforcement learning in episodic environments with high-dimensional observations. We present new sample efficient algorithms for environments with deterministic hidden state dynamics but stochastic rich observations. These methods represent computationally efficient alternatives to prior algorithms that rely on enumerating exponentially many functions. We show that the only known statistically efficient algorithm for the more general stochastic transition setting requires NP-hard computation which cannot be implemented via standard optimization primitives. We also present several examples that illustrate fundamental challenges of tractable PAC reinforcement learning in such general settings.

Connected through hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning"):
  with members:
    - "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"

Connected through hyperedge HE-2 (induced by source paper "is a good representation sufficient for sample efficient reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "provably efficient q learning with function approximation via distribution shift error checking oracle"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "learning to control in metric space with optimal regret"
    - "on exact computation with an infinitely wide neural net"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-4 (induced by source paper "dual policy iteration"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "thinking fast and slow with deep learning and tree search"
    - "guided policy search as approximate mirror descent"
    - "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"
    - "learning to filter with predictive state inference machines"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "policy certificates towards accountable reinforcement learning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "is q learning provably efficient"
    - "multi step off policy learning without importance sampling ratios"
    - "fairness incentives for myopic agents"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"

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)