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: "value function approximation in noisy environments using locally smoothed regularized approximate linear programs"
  Abstract: Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear Programming (RALP) could produce value functions and policies which compared favorably to established linear value function approximation techniques like LSPI. RALP's success primarily stems from the ability to solve the feature selection and value function approximation steps simultaneously. RALP's performance guarantees become looser if sampled next states are used. For very noisy domains, RALP requires an accurate model rather than samples, which can be unrealistic in some practical scenarios. In this paper, we demonstrate this weakness, and then introduce Locally Smoothed L1-Regularized Approximate Linear Programming (LS-RALP). We demonstrate that LS-RALP mitigates inaccuracies stemming from noise even without an accurate model. We show that, given some smoothness assumptions, as the number of samples increases, error from noise approaches zero, and provide experimental examples of LS-RALP's success on common reinforcement learning benchmark problems.

Connected through hyperedge HE-1 (induced by source paper "scalable bilinear pi learning using state and action features"):
  with members:
    - "boosting the actor with dual critic"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"
    - "stochastic variance reduction methods for policy evaluation"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "learning from conditional distributions via dual embeddings"
    - "taming the noise in reinforcement learning via soft updates"
    - "trust region policy optimization"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-2 (induced by source paper "learning nash equilibrium for general sum markov games from batch data"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "modelling transition dynamics in mdps with rkhs embeddings"
    - "value function approximation in zero sum markov games"

Connected through hyperedge HE-3 (induced by source paper "an analysis of state relevance weights and sampling distributions on l1 regularized approximate linear programming approximation accuracy"):
  with members:
    - "sparse q learning with mirror descent"
    - "feature selection using regularization in approximate linear programs for markov decision processes"

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)