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 oracle efficient pac rl with rich observations"
  Abstract: We study the computational tractability of PAC reinforcement learning with rich observations. We present new provably sample-efficient algorithms for environments with deterministic hidden state dynamics and stochastic rich observations. These methods operate in an oracle model of computation -- accessing policy and value function classes exclusively through standard optimization primitives -- and therefore represent computationally efficient alternatives to prior algorithms that require enumeration. With stochastic hidden state dynamics, we prove that the only known sample-efficient algorithm, OLIVE, cannot be implemented in the oracle model. 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 "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-2 (induced by source paper "learning near optimal policies with low inherent bellman error"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"

Connected through hyperedge HE-3 (induced by source paper "frequentist regret bounds for randomized least squares value iteration"):
  with members:
    - "learning near optimal policies with low inherent bellman error"
    - "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"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "is a good representation sufficient for sample efficient reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "optimality and approximation with policy gradient methods in markov decision processes"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"
    - "global optimality guarantees for policy gradient methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "understanding the impact of entropy on policy optimization"
    - "global convergence of policy gradient methods for linearized control problems"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-5 (induced by source paper "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"):
  with members:
    - "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"
    - "stochastic approximation with cone contractive operators sharp ell_ infty bounds for q learning"
    - "information theoretic considerations in batch reinforcement learning"
    - "is q learning provably efficient"
    - "global convergence of policy gradient methods for linearized control problems"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "minimax weight and q function learning for off policy evaluation"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "on value functions and the agent environment boundary"
    - "batch policy learning under constraints"
    - "information theoretic considerations in batch reinforcement learning"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "a kernel loss for solving the bellman equation"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-8 (induced by source paper "pac reinforcement learning without real world feedback"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

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)