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: "path consistency learning in tsallis entropy regularized mdps"
  Abstract: We study the sparse entropy-regularized reinforcement learning (ERL) problem in which the entropy term is a special form of the Tsallis entropy. The optimal policy of this formulation is sparse, i.e.,~at each state, it has non-zero probability for only a small number of actions. This addresses the main drawback of the standard Shannon entropy-regularized RL (soft ERL) formulation, in which the optimal policy is softmax, and thus, may assign a non-negligible probability mass to non-optimal actions. This problem is aggravated as the number of actions is increased. In this paper, we follow the work of Nachum et al. (2017) in the soft ERL setting, and propose a class of novel path consistency learning (PCL) algorithms, called {\em sparse PCL}, for the sparse ERL problem that can work with both on-policy and off-policy data. We first derive a {\em sparse consistency} equation that specifies a relationship between the optimal value function and policy of the sparse ERL along any system trajectory. Crucially, a weak form of the converse is also true, and we quantify the sub-optimality of a policy which satisfies sparse consistency, and show that as we increase the number of actions, this sub-optimality is better than that of the soft ERL optimal policy. We then use this result to derive the sparse PCL algorithms. We empirically compare sparse PCL with its soft counterpart, and show its advantage, especially in problems with a large number of actions.

Connected through hyperedge HE-1 (induced by source paper "multi path policy optimization"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "diversity inducing policy gradient using maximum mean discrepancy to find a set of diverse policies"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "addressing function approximation error in actor critic methods"
    - "policy search in continuous action domains an overview"

Connected through hyperedge HE-2 (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"
    - "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"
    - "trust pcl an off policy trust region method for continuous control"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "f divergence inequalities"

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"
    - "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"
    - "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

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)