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: "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
  Abstract: In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved: (i) how to design reliable, convergent, and robust reinforcement learning algorithms (ii) how to guarantee that reinforcement learning satisfies pre-specified "safety" guarantees, and remains in a stable region of the parameter space (iii) how to design "off-policy" temporal difference learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of stochastic optimization. In this paper, we provide detailed answers to all these questions using the powerful framework of proximal operators. #R##N#The key idea that emerges is the use of primal dual spaces connected through the use of a Legendre transform. This allows temporal difference updates to occur in dual spaces, allowing a variety of important technical advantages. The Legendre transform elegantly generalizes past algorithms for solving reinforcement learning problems, such as natural gradient methods, which we show relate closely to the previously unconnected framework of mirror descent methods. Equally importantly, proximal operator theory enables the systematic development of operator splitting methods that show how to safely and reliably decompose complex products of gradients that occur in recent variants of gradient-based temporal difference learning. This key technical innovation makes it possible to finally design "true" stochastic gradient methods for reinforcement learning. Finally, Legendre transforms enable a variety of other benefits, including modeling sparsity and domain geometry. Our work builds extensively on recent work on the convergence of saddle-point algorithms, and on the theory of monotone operators.

Connected through hyperedge HE-1 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "a nonparametric offpolicy policy gradient"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-2 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "addressing function approximation error in actor critic methods"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "multi agent reinforcement learning via double averaging primal dual optimization"

Connected through hyperedge HE-3 (induced by source paper "provably convergent off policy actor critic with function approximation"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "entropic regularization of markov decision processes"):
  with members:
    - "a theory of regularized markov decision processes"
    - "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning"
    - "maximum causal tsallis entropy imitation learning"
    - "an elementary introduction to information geometry"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "target based temporal difference learning"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "finite sample analyses for td 0 with function approximation"
    - "learning from conditional distributions via dual embeddings"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
    - "practical kernel based reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "zap q learning with nonlinear function approximation"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "tracking control by the newton raphson flow applications to autonomous vehicles"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "concentration bounds for two timescale stochastic approximation with applications to reinforcement learning"
    - "fastest convergence for q learning"
    - "stability of stochastic approximations with controlled markov noise and temporal difference learning"

Connected through hyperedge HE-8 (induced by source paper "online off policy prediction"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep reinforcement learning that matters"
    - "multi step off policy learning without importance sampling ratios"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "on convergence of some gradient based temporal differences algorithms for off policy learning"
    - "sample efficient actor critic with experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"

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)