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: "physics as inverse graphics joint unsupervised learning of objects and physics from video"
  Abstract: We aim to perform unsupervised discovery of objects and their states such as location and velocity, as well as physical system parameters such as mass and gravity from video -- given only the differential equations governing the scene dynamics. Existing physical scene understanding methods require either object state supervision, or do not integrate with differentiable physics to learn interpretable system parameters and states. We address this problem through a $\textit{physics-as-inverse-graphics}$ approach that brings together vision-as-inverse-graphics and differentiable physics engines. This framework allows us to perform long term extrapolative video prediction, as well as vision-based model-predictive control. Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems). We further show the value of this tight vision-physics integration by demonstrating data-efficient learning of vision-actuated model-based control for a pendulum system. The controller's interpretability also provides unique capabilities in goal-driven control and physical reasoning for zero-data adaptation.

Connected through hyperedge HE-1 (induced by source paper "structured object aware physics prediction for video modeling and planning"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "monet unsupervised scene decomposition and representation"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "relational action forecasting"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "vid2param modelling of dynamics parameters from video"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "active localization of gas leaks using fluid simulation"
    - "interpretable latent spaces for learning from demonstration"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "the orca hub explainable offshore robotics through intelligent interfaces"

Connected through hyperedge HE-3 (induced by source paper "vid2param online system identification from video for robotics applications"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "interpretable latent spaces for learning from demonstration"
    - "neural lander stable drone landing control using learned dynamics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-4 (induced by source paper "learning symbolic physics with graph networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"
    - "relational inductive biases deep learning and graph networks"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical 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)