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: "information theoretic considerations in batch reinforcement learning"
  Abstract: Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucially rely on two types of assumptions: (1) mild distribution shift, and (2) representation conditions that are stronger than realizability. However, the necessity ("why do we need them?") and the naturalness ("when do they hold?") of such assumptions have largely eluded the literature. In this paper, we revisit these assumptions and provide theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation.

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 "policy aware model learning for policy gradient methods"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "gradient aware model based policy search"
    - "global optimality guarantees for policy gradient methods"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "minimax confidence interval for off policy evaluation and policy optimization"):
  with members:
    - "reinforcement learning via fenchel rockafellar duality"
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "understanding the curse of horizon in off policy evaluation via conditional importance sampling"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "conditional importance sampling for off policy learning"

Connected through hyperedge HE-4 (induced by source paper "reward free exploration for reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "reinforcement learning with convex constraints"
    - "provably efficient exploration in policy optimization"
    - "kinematic state abstraction and provably efficient rich observation reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "reward constrained policy optimization"

Connected through hyperedge HE-5 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "sample optimal parametric q learning using linearly additive features"
    - "minimax weight and q function learning for off policy evaluation"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "a kernel loss for solving the bellman equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "is the bellman residual a bad proxy"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-6 (induced by source paper "neural policy gradient methods global optimality and rates of convergence"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "a theoretical analysis of deep q learning"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-7 (induced by source paper "provably efficient exploration in policy optimization"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"

Connected through hyperedge HE-8 (induced by source paper "provably efficient imitation learning from observation alone"):
  with members:
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "behavioral cloning from observation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "agile autonomous driving using end to end deep imitation learning"

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)