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: "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning"
  Abstract: In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyze the optimality condition of a sparse MDP. Then, we propose a sparse value iteration method which solves a sparse MDP and then prove the convergence and optimality of sparse value iteration using the Banach fixed point theorem. The proposed sparse MDP is compared to soft MDPs which utilize causal entropy regularization. We show that the performance error of a sparse MDP has a constant bound, while the error of a soft MDP increases logarithmically with respect to the number of actions, where this performance error is caused by the introduced regularization term. In experiments, we apply sparse MDPs to reinforcement learning problems. The proposed method outperforms existing methods in terms of the convergence speed and performance.

Connected through hyperedge HE-1 (induced by source paper "off policy actor critic in an ensemble achieving maximum general entropy and effective environment exploration in deep reinforcement learning"):
  with members:
    - "effective exploration for deep reinforcement learning via bootstrapped q ensembles under tsallis entropy regularization"
    - "an adaptive clipping approach for proximal policy optimization"
    - "ucb exploration via q ensembles"
    - "sim to real learning agile locomotion for quadruped robots"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning"):
  with members:
    - "effective exploration for deep reinforcement learning via bootstrapped q ensembles under tsallis entropy regularization"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "equivalence between policy gradients and soft q learning"
    - "boltzmann exploration done right"
    - "bridging the gap between value and policy based reinforcement learning"

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"
    - "a unified view of entropy regularized markov decision processes"
    - "proximal policy optimization algorithms"
    - "f divergence inequalities"

Connected through hyperedge HE-5 (induced by source paper "revisiting the softmax bellman operator new benefits and new perspective"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "a unified view of entropy regularized markov decision processes"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "effective exploration for deep reinforcement learning via bootstrapped q ensembles under tsallis entropy regularization"):
  with members:
    - "an adaptive clipping approach for proximal policy optimization"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "equivalence between policy gradients and soft q learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "path consistency learning in tsallis entropy regularized mdps"):
  with members:
    - "trust pcl an off policy trust region method for continuous control"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "asynchronous methods for deep reinforcement learning"
    - "pgq combining policy gradient and q learning"
    - "an alternative softmax operator for reinforcement learning"
    - "trust region policy optimization"
    - "dynamic policy programming"

Connected through hyperedge HE-8 (induced by source paper "maximum causal tsallis entropy imitation learning"):
  with members:
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "robust imitation of diverse behaviors"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generative adversarial imitation learning"
    - "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)