human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "combining physical simulators and object based networks for control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to manipulate object collections using grounded state representations")
  Member "monet unsupervised scene decomposition and representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "task agnostic dynamics priors for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neuro evolutionary frameworks for generalized learning agents")
  Member "unsupervised learning of object keypoints for perception and control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised learning of object structure and dynamics from videos")
  Member "object oriented dynamics learning through multi level abstraction":
    also connected through other hyperedges:
      (none)
  Member "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "deep set prediction networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learn to predict sets using feed forward neural networks")
  Member "unsupervised discovery of parts structure and dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep variational luenberger type observer for stochastic video prediction")

Question: Please tell me which class the source paper 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)