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: "provably efficient rl with rich observations via latent state decoding"
  Abstract: We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states. Under certain identifiability assumptions, we demonstrate how to estimate a mapping from the observations to latent states inductively through a sequence of regression and clustering steps---where previously decoded latent states provide labels for later regression problems---and use it to construct good exploration policies. We provide finite-sample guarantees on the quality of the learned state decoding function and exploration policies, and complement our theory with an empirical evaluation on a class of hard exploration problems. Our method exponentially improves over $Q$-learning with naive exploration, even when $Q$-learning has cheating access to latent states.

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 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"
    - "a theoretical analysis of deep q learning"

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 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"
    - "learning to control in metric space with optimal regret"

Connected through hyperedge HE-3 (induced by source paper "reward free exploration for reinforcement learning"):
  with members:
    - "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"
    - "reward constrained policy optimization"

Connected through hyperedge HE-4 (induced by source paper "explicit explore exploit algorithms in continuous state spaces"):
  with members:
    - "self supervised exploration via disagreement"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "model based active exploration"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "practical contextual bandits with regression oracles"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-5 (induced by source paper "is a good representation sufficient for sample efficient reinforcement learning"):
  with members:
    - "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"
    - "comments on the du kakade wang yang lower bounds"

Connected through hyperedge HE-6 (induced by source paper "provably efficient q learning with function approximation via distribution shift error checking oracle"):
  with members:
    - "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"
    - "on polynomial time pac reinforcement learning with rich observations"

Connected through hyperedge HE-7 (induced by source paper "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"):
  with members:
    - "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"
    - "on oracle efficient pac rl with rich observations"
    - "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-8 (induced by source paper "pac reinforcement learning without real world feedback"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "on oracle efficient pac rl with rich observations"
    - "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)