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: "entity abstraction in visual model based reinforcement learning"
  Abstract: This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before. We present object-centric perception, prediction, and planning (OP3), which to the best of our knowledge is the first entity-centric dynamic latent variable framework for model-based reinforcement learning that acquires entity representations from raw visual observations without supervision and uses them to predict and plan. OP3 enforces entity-abstraction -- symmetric processing of each entity representation with the same locally-scoped function -- which enables it to scale to model different numbers and configurations of objects from those in training. Our approach to solving the key technical challenge of grounding these entity representations to actual objects in the environment is to frame this variable binding problem as an inference problem, and we developing an interactive inference algorithm that uses temporal continuity and interactive feedback to bind information about object properties to the entity variables. On block-stacking tasks, OP3 generalizes to novel block configurations and more objects than observed during training, outperforming an oracle model that assumes access to object supervision and achieving two to three times better accuracy than a state-of-the-art video prediction model.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "emergent systematic generalization in a situated agent"
    - "monet unsupervised scene decomposition and representation"

Connected through hyperedge HE-2 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "unsupervised state representation learning in atari"
    - "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-3 (induced by source paper "investigating simple object representations in model free deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

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)