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: "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
  Abstract: Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for theoretically measuring the performance of such algorithms called Uniform-PAC, which is a strengthening of the classical Probably Approximately Correct (PAC) framework. In contrast to the PAC framework, the uniform version may be used to derive high probability regret guarantees and so forms a bridge between the two setups that has been missing in the literature. We demonstrate the benefits of the new framework for finite-state episodic MDPs with a new algorithm that is Uniform-PAC and simultaneously achieves optimal regret and PAC guarantees except for a factor of the horizon.

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 "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-3 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-4 (induced by source paper "optimistic policy optimization with bandit feedback"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on connections between constrained optimization and reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "provable self play algorithms for competitive reinforcement learning"):
  with members:
    - "reward free exploration for reinforcement learning"
    - "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"
    - "online convex optimization in adversarial markov decision processes"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "corruption robust exploration in episodic reinforcement learning"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "feature based q learning for two player stochastic games"

Connected through hyperedge HE-6 (induced by source paper "asymptotically efficient off policy evaluation for tabular reinforcement learning"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "representation balancing mdps for off policy policy evaluation"
    - "is q learning provably efficient"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "regret bounds for discounted mdps"):
  with members:
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "is q learning provably efficient"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "information theoretic considerations in batch reinforcement learning"
    - "provably efficient exploration in policy optimization"
    - "kinematic state abstraction and provably efficient rich observation reinforcement learning"
    - "provably efficient maximum entropy exploration"

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)