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: "combined reinforcement learning via abstract representations"
  Abstract: In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

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"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"

Connected through hyperedge HE-2 (induced by source paper "resolving spurious correlations in causal models of environments via interventions"):
  with members:
    - "learning causal state representations of partially observable environments"
    - "causal confusion in imitation learning"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "the consciousness prior"
    - "proximal policy optimization algorithms"
    - "concrete problems in ai safety"

Connected through hyperedge HE-3 (induced by source paper "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (induced by source paper "deepmdp learning continuous latent space models for representation learning"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "model based reinforcement learning for atari"
    - "the value function polytope in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-5 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "the value function polytope in reinforcement learning"
    - "the laplacian in rl learning representations with efficient approximations"
    - "dopamine a research framework for deep reinforcement learning"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "deep reinforcement learning and the deadly triad"

Connected through hyperedge HE-6 (induced by source paper "learning action transferable policy with action embedding"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "domain adaptation for reinforcement learning on the atari"
    - "learning latent dynamics for planning from pixels"
    - "deep contextualized word representations"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "universal successor representations for transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning first order symbolic representations for planning from the structure of the state space"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model free model based and general intelligence"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "transfer of deep reactive policies for mdp planning"
    - "learning features and abstract actions for computing generalized plans"
    - "deep learning a critical appraisal"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-8 (induced by source paper "successor features combine elements of model free and model based reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "learning the reward function for a misspecified model"
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "imagination augmented agents for deep 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)