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: "unsupervised state representation learning in atari"
  Abstract: State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"
    - "combined reinforcement learning via abstract representations"

Connected through hyperedge HE-2 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "model based reinforcement learning for atari"
    - "on variational bounds of mutual information"
    - "prediction consistency curvature representation learning for locally linear control"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "learning latent dynamics for planning from pixels"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-3 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "object sensitive deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "synthesizing neural network controllers with probabilistic model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"
    - "deepmdp learning continuous latent space models for representation learning"

Connected through hyperedge HE-5 (induced by source paper "video representation learning by dense predictive coding"):
  with members:
    - "self supervised learning for video correspondence flow"
    - "learning correspondence from the cycle consistency of time"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "slowfast networks for video recognition"
    - "self supervised video representation learning with space time cubic puzzles"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-6 (induced by source paper "online learned continual compression with stacked quantization modules"):
  with members:
    - "decoupled greedy learning of cnns"
    - "generating diverse high fidelity images with vq vae 2"
    - "continual learning with tiny episodic memories"
    - "compressing the input for cnns with the first order scattering transform"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "deep generative modeling of lidar data"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-7 (induced by source paper "learnt dynamics generalizes across tasks datasets and populations"):
  with members:
    - "on mutual information maximization for representation learning"
    - "detecting abnormalities in resting state dynamics an unsupervised learning approach"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "prediction of progression to alzheimer s disease with deep infomax"
    - "rethinking imagenet pre training"
    - "machine learning in resting state fmri analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)