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: "is the bellman residual a bad proxy"
  Abstract: This paper aims at theoretically and empirically comparing two standard optimization criteria for Reinforcement Learning: i) maximization of the mean value and ii) minimization of the Bellman residual. For that purpose, we place ourselves in the framework of policy search algorithms, that are usually designed to maximize the mean value, and derive a method that minimizes the residual $\|T_* v_\pi - v_\pi\|_{1,\nu}$ over policies. A theoretical analysis shows how good this proxy is to policy optimization, and notably that it is better than its value-based counterpart. We also propose experiments on randomly generated generic Markov decision processes, specifically designed for studying the influence of the involved concentrability coefficient. They show that the Bellman residual is generally a bad proxy to policy optimization and that directly maximizing the mean value is much better, despite the current lack of deep theoretical analysis. This might seem obvious, as directly addressing the problem of interest is usually better, but given the prevalence of (projected) Bellman residual minimization in value-based reinforcement learning, we believe that this question is worth to be considered.

Connected through hyperedge HE-1 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "sample optimal parametric q learning using linearly additive features"
    - "minimax weight and q function learning for off policy evaluation"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "a kernel loss for solving the bellman equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-2 (induced by source paper "deep residual reinforcement learning"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "world models"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "diagnosing bottlenecks in deep q learning algorithms"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning and the deadly triad"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "the utility of sparse representations for control in reinforcement learning"
    - "policy optimization via importance sampling"

Connected through hyperedge HE-4 (induced by source paper "on connections between constrained optimization and reinforcement learning"):
  with members:
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "deep conservative policy iteration"
    - "a theory of regularized markov decision processes"
    - "a unified view of entropy regularized markov decision processes"
    - "trust region policy optimization"

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)