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: "an alternative softmax operator for reinforcement learning"
  Abstract: A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice.

Connected through hyperedge HE-1 (induced by source paper "deep rbf value functions for continuous control"):
  with members:
    - "caql continuous action q learning"
    - "the problem with ddpg understanding failures in deterministic environments with sparse rewards"
    - "meta gradient reinforcement learning"
    - "revisiting the softmax bellman operator new benefits and new perspective"
    - "lipschitz continuity in model based reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "actor expert a framework for using action value methods in continuous action spaces"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a general framework for learning mean field games"):
  with members:
    - "q learning for mean field controls"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "a theory of regularized markov decision processes"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "real time bidding by reinforcement learning in display advertising"
    - "reinforcement learning with deep energy based policies"
    - "a unified view of entropy regularized markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "chainerrl a deep reinforcement learning library"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "catalyst rl a distributed framework for reproducible rl research"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "dopamine a research framework for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "maximum entropy regularized multi goal reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-5 (induced by source paper "a theory of regularized markov decision processes"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic algorithms and applications"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "relative entropy regularized policy iteration"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-6 (induced by source paper "long term visitation value for deep exploration in sparse reward reinforcement learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "exploration by random network distillation"
    - "is q learning provably efficient"
    - "parameter space noise for exploration"
    - "a tutorial on thompson sampling"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "time sensitive bandit learning and satisficing thompson sampling"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning with dynamic boltzmann softmax updates"):
  with members:
    - "meta gradient reinforcement learning"
    - "revisiting the softmax bellman operator new benefits and new perspective"
    - "learning to walk via deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "boltzmann exploration done right"
    - "reinforcement learning with deep energy based policies"
    - "pgq combining policy gradient and q learning"

Connected through hyperedge HE-8 (induced by source paper "crossnorm normalization for off policy td reinforcement learning"):
  with members:
    - "towards characterizing divergence in deep q learning"
    - "deepmind control suite"
    - "off policy deep reinforcement learning without exploration"
    - "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"
    - "distributed distributional deterministic policy gradients"
    - "hindsight experience replay"

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)