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: "learning visual predictive models of physics for playing billiards"
  Abstract: The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents. In this paper, we explore how an agent can be equipped with an internal model of the dynamics of the external world, and how it can use this model to plan novel actions by running multiple internal simulations ("visual imagination"). Our models directly process raw visual input, and use a novel object-centric prediction formulation based on visual glimpses centered on objects (fixations) to enforce translational invariance of the learned physical laws. The agent gathers training data through random interaction with a collection of different environments, and the resulting model can then be used to plan goal-directed actions in novel environments that the agent has not seen before. We demonstrate that our agent can accurately plan actions for playing a simulated billiards game, which requires pushing a ball into a target position or into collision with another ball.

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning generalizable physical dynamics of 3d rigid objects"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-3 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "environment probing interaction policies"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "single episode policy transfer in reinforcement learning"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"

Connected through hyperedge HE-4 (induced by source paper "let s push things forward a survey on robot pushing"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "feature based transfer learning for robotic push manipulation"
    - "a data efficient approach to precise and controlled pushing"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "deep predictive policy training using reinforcement learning"
    - "a probabilistic data driven model for planar pushing"

Connected through hyperedge HE-5 (induced by source paper "the surprising effectiveness of linear models for visual foresight in object pile manipulation"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "planning robot motion using deep visual prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "object centric forward modeling for model predictive control"
    - "learning to manipulate object collections using grounded state representations"
    - "difftaichi differentiable programming for physical simulation"
    - "friction variability in planar pushing data anisotropic friction and data collection bias"
    - "reasoning about physical interactions with object oriented prediction and planning"

Connected through hyperedge HE-6 (induced by source paper "belief regulated dual propagation nets for learning action effects on groups of articulated objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "propagation networks for model based control under partial observation"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "interaction networks for learning about objects relations and physics"

Connected through hyperedge HE-7 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-8 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

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.CV(Computer Vision and Pattern Recognition)