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: "a short variational proof of equivalence between policy gradients and soft q learning"
  Abstract: Two main families of reinforcement learning algorithms, Q-learning and policy gradients, have recently been proven to be equivalent when using a softmax relaxation on one part, and an entropic regularization on the other. We relate this result to the well-known convex duality of Shannon entropy and the softmax function. Such a result is also known as the Donsker-Varadhan formula. This provides a short proof of the equivalence. We then interpret this duality further, and use ideas of convex analysis to prove a new policy inequality relative to soft Q-learning.

Connected through hyperedge HE-1 (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"
    - "equivalence between policy gradients and soft q learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "sample efficient reinforcement learning with maximum entropy mellowmax episodic control"):
  with members:
    - "ucb exploration via q ensembles"
    - "deep reinforcement learning a brief survey"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "building machines that learn and think like people"

Connected through hyperedge HE-3 (induced by source paper "variational bayesian reinforcement learning with regret bounds"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "equivalence between policy gradients and soft q learning"
    - "the uncertainty bellman equation and exploration"
    - "boltzmann exploration done right"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"

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)