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: "feature selection using regularization in approximate linear programs for markov decision processes"
  Abstract: Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions reliably. Large and rich sets of features can cause existing algorithms to overfit because of a limited number of samples. We address this shortcoming using $L_1$ regularization in approximate linear programming. Because the proposed method can automatically select the appropriate richness of features, its performance does not degrade with an increasing number of features. These results rely on new and stronger sampling bounds for regularized approximate linear programs. We also propose a computationally efficient homotopy method. The empirical evaluation of the approach shows that the proposed method performs well on simple MDPs and standard benchmark problems.

Connected through hyperedge HE-1 (induced by source paper "representation learning on graphs a reinforcement learning application"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "struc2vec learning node representations from structural identity"
    - "representation learning on graphs methods and applications"
    - "efficient estimation of word representations in vector space"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "temporal regularization in markov decision process"):
  with members:
    - "deep reinforcement learning that matters"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "a unified view of entropy regularized markov decision processes"
    - "proximal policy optimization algorithms"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "the arcade learning environment an evaluation platform for general agents"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-3 (induced by source paper "mitigating planner overfitting in model based reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-4 (induced by source paper "a linearly relaxed approximate linear program for markov decision processes"):
  with members:
    - "markov decision processes with applications in wireless sensor networks a survey"
    - "a generalized reduced linear program for markov decision processes"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-5 (induced by source paper "an analysis of state relevance weights and sampling distributions on l1 regularized approximate linear programming approximation accuracy"):
  with members:
    - "value function approximation in noisy environments using locally smoothed regularized approximate linear programs"
    - "sparse q learning with mirror descent"

Connected through hyperedge HE-6 (induced by source paper "a generalized reduced linear program for markov decision processes"):
  with members:
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-7 (induced by source paper "sparse q learning with mirror descent"):
  with members:
    - "on the universality of online mirror descent"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-8 (induced by source paper "greedy algorithms for sparse reinforcement learning"):
  with members:
    - "orthogonal matching pursuit with replacement"

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.AI(Artificial Intelligence)