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 to represent mechanics via long term extrapolation and interpolation"
  Abstract: While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters. In order to adopt such models for artificial intelligence, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortunately, such approaches can be unsuitable for modeling complex real-world scenarios, where manually authoring relevant state spaces tend to be challenging. In this work, we investigate if neural networks can implicitly learn physical states of real-world mechanical processes only based on visual data, and thus enable long-term physical extrapolation. We develop a recurrent neural network architecture for this task and also characterize resultant uncertainties in the form of evolving variance estimates. We evaluate our setup to extrapolate motion of a rolling ball on bowl of varying shape and orientation using only images as input, and report competitive results with approaches that assume access to internal physics models and parameters.

Connected through hyperedge HE-1 (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-2 (induced by source paper "neural re simulation for generating bounces in single images"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "neural rerendering in the wild"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "ignor image guided neural object rendering"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "interpolations of smoke and liquid simulations"

Connected through hyperedge HE-3 (induced by source paper "learning generalizable physical dynamics of 3d rigid objects"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "physical primitive decomposition"
    - "interpretable intuitive physics model"
    - "unsupervised intuitive physics from visual observations"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-4 (induced by source paper "bounce and learn modeling scene dynamics with real world bounces"):
  with members:
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to fly by crashing"
    - "forecasting human dynamics from static images"
    - "visual semantic planning using deep successor representations"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "smash physics guided reconstruction of collisions from videos"

Connected through hyperedge HE-5 (induced by source paper "unsupervised intuitive physics from visual observations"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "graph networks as learnable physics engines for inference and control"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "stochastic adversarial video prediction"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "predicting deeper into the future of semantic segmentation"
    - "learning a physical long term predictor"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

Connected through hyperedge HE-6 (induced by source paper "taking visual motion prediction to new heightfields"):
  with members:
    - "learning a physical long term predictor"
    - "learning to generate long term future via hierarchical prediction"
    - "recurrent environment simulators"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "the predictron end to end learning and planning"
    - "smash physics guided reconstruction of collisions from videos"
    - "accelerating eulerian fluid simulation with convolutional networks"

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)