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: "ucb and infogain exploration via boldsymbol q ensembles"
  Abstract: We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. First we propose an exploration strategy based on upper-confidence bounds (UCB). Next, we define an ''InfoGain'' exploration bonus, which depends on the disagreement of the $Q$-ensemble. Our experiments show significant gains on the Atari benchmark.

Connected through hyperedge HE-1 (induced by source paper "optimistic exploration even with a pessimistic initialisation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "model based active exploration"
    - "is q learning provably efficient"

Connected through hyperedge HE-2 (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"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (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-4 (induced by source paper "kinematic state abstraction and provably efficient rich observation reinforcement learning"):
  with members:
    - "sample complexity of reinforcement learning using linearly combined model ensembles"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "ucb exploration via q ensembles"
    - "learning to reason end to end module networks for visual question answering"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-5 (induced by source paper "distributional reinforcement learning for efficient exploration"):
  with members:
    - "deep reinforcement learning with decorrelation"
    - "reinforcing classical planning for adversary driving scenarios"
    - "ucb exploration via q ensembles"
    - "quota the quantile option architecture for reinforcement learning"
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "sample efficient reinforcement learning with maximum entropy mellowmax episodic control"):
  with members:
    - "ucb exploration via q ensembles"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "deep reinforcement learning a brief survey"
    - "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-7 (induced by source paper "maximizing overall diversity for improved uncertainty estimates in deep ensembles"):
  with members:
    - "ucb exploration via q ensembles"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep exploration via bootstrapped dqn"
    - "wide residual networks"
    - "bayesian dark knowledge"
    - "why m heads are better than one training a diverse ensemble of deep networks"

Connected through hyperedge HE-8 (induced by source paper "variational bayes a report on approaches and applications"):
  with members:
    - "ucb exploration via q ensembles"
    - "noisy networks for exploration"
    - "deep exploration via bootstrapped dqn"
    - "density estimation using real nvp"

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)