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: "vfunc a deep generative model for functions"
  Abstract: We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and parameter noise. In this short paper we describe our model, situate it in the context of prior work, and present proof-of-concept experiments for regression and RL.

Connected through hyperedge HE-1 (induced by source paper "marginalized state distribution entropy regularization in policy optimization"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "provably efficient maximum entropy exploration"
    - "count based exploration with the successor representation"
    - "addressing function approximation error in actor critic methods"
    - "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"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-2 (induced by source paper "entropy regularization with discounted future state distribution in policy gradient methods"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "the value function polytope in reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "addressing function approximation error in actor critic methods"
    - "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"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-3 (induced by source paper "attentive neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "image transformer"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "learning models for visual 3d localization with implicit mapping"
    - "variational memory addressing in generative models"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "transfer learning by modeling a distribution over policies"):
  with members:
    - "safe option critic learning safety in the option critic architecture"
    - "meta reinforcement learning of structured exploration strategies"
    - "latent space policies for hierarchical reinforcement learning"
    - "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"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "distral robust multitask reinforcement learning"

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)