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: "virel a variational inference framework for reinforcement learning"
  Abstract: Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference frameworks and their algorithms pose significant challenges for learning optimal policies, e.g., the absence of mode capturing behaviour in pseudo-likelihood methods and difficulties learning deterministic policies in maximum entropy RL based approaches. We propose VIREL, a novel, theoretically grounded probabilistic inference framework for RL that utilises a parametrised action-value function to summarise future dynamics of the underlying MDP. This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps. In applying variational expectation-maximisation to VIREL we thus show that the actor-critic algorithm can be reduced to expectation-maximisation, with policy improvement equivalent to an E-step and policy evaluation to an M-step. We then derive a family of actor-critic methods from VIREL, including a scheme for adaptive exploration. Finally, we demonstrate that actor-critic algorithms from this family outperform state-of-the-art methods based on soft value functions in several domains.

Connected through hyperedge HE-1 (induced by source paper "making sense of reinforcement learning and probabilistic inference"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "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"
    - "variational bayesian reinforcement learning with regret bounds"
    - "the uncertainty bellman equation and exploration"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-2 (induced by source paper "maven multi agent variational exploration"):
  with members:
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "cesma centralized expert supervises multi agents"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "coordinated exploration in concurrent reinforcement learning"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep active inference as variational policy gradients"):
  with members:
    - "bayesian policy selection using active inference"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "expanding the active inference landscape more intrinsic motivations in the perception action loop"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "boltzmann exploration done right"

Connected through hyperedge HE-4 (induced by source paper "hierarchical variational imitation learning of control programs"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational option discovery algorithms"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "robustfill neural program learning under noisy i o"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "generative adversarial imitation learning"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-5 (induced by source paper "stochastically dominant distributional reinforcement learning"):
  with members:
    - "being optimistic to be conservative quickly learning a cvar policy"
    - "policy optimization as wasserstein gradient flows"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-6 (induced by source paper "quantile regression deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "policy optimization as wasserstein gradient flows"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "autoregressive quantile networks for generative modeling"
    - "implicit quantile networks for distributional reinforcement learning"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "distributional reinforcement learning with quantile regression"

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)