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: "compress and control"
  Abstract: This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a corresponding estimate of value. Under appropriate stationarity and ergodicity conditions, we show that the use of a sufficiently powerful model gives rise to a consistent value function estimator. We also study the behavior of this technique when applied to various Atari 2600 video games, where the use of suboptimal modeling techniques is unavoidable. We consider three fundamentally different models, all too limited to perfectly model the dynamics of the system. Remarkably, we find that our technique provides sufficiently accurate value estimates for effective on-policy control. We conclude with a suggestive study highlighting the potential of our technique to scale to large problems.

Connected through hyperedge HE-1 (induced by source paper "online learning in contextual bandits using gated linear networks"):
  with members:
    - "gated linear networks"
    - "parameter space noise for exploration"
    - "online learning with gated linear networks"
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "unifying count based exploration and intrinsic motivation"
    - "linear and geometric mixtures analysis"

Connected through hyperedge HE-2 (induced by source paper "td or not td analyzing the role of temporal differencing in deep reinforcement learning"):
  with members:
    - "deep reinforcement learning that matters"
    - "asynchronous methods for deep reinforcement learning"
    - "building machines that learn and think like people"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

Connected through hyperedge HE-3 (induced by source paper "revisiting the arcade learning environment evaluation protocols and open problems for general agents"):
  with members:
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "feudal networks for hierarchical reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "recurrent environment simulators"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "graying the black box understanding dqns"

Connected through hyperedge HE-4 (induced by source paper "a distributional perspective on reinforcement learning"):
  with members:
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "pixel recurrent neural networks"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "online learning with gated linear networks"):
  with members:
    - "introduction to online convex optimization"
    - "deep online convex optimization with gated games"
    - "efficient second order online learning by sketching"
    - "an architecture for deep hierarchical generative models"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "linear and geometric mixtures analysis"
    - "mixing strategies in data compression"
    - "context tree switching"

Connected through hyperedge HE-6 (induced by source paper "aixijs a software demo for general reinforcement learning"):
  with members:
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "unifying count based exploration and intrinsic motivation"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "thompson sampling is asymptotically optimal in general environments"
    - "vime variational information maximizing exploration"
    - "pixel recurrent neural networks"
    - "state of the art control of atari games using shallow reinforcement learning"
    - "prioritized experience replay"
    - "increasing the action gap new operators for reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"

Connected through hyperedge HE-8 (induced by source paper "a deep learning approach for joint video frame and reward prediction in atari games"):
  with members:
    - "curiosity driven exploration by self supervised prediction"
    - "imagination augmented agents for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "unifying count based exploration and intrinsic motivation"
    - "unsupervised learning of video representations using lstms"
    - "deep convolutional inverse graphics network"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "action conditional video prediction using deep networks in atari games"

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)