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: "smash physics guided reconstruction of collisions from videos"
  Abstract: Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized. After tedious trial-and-error iterations, when objects can actually be made to collide, then they are difficult to capture in 3D. In contrast, synthetically generating plausible collisions is difficult as it requires adjusting different collision parameters (e.g., object mass ratio, coefficient of restitution, etc.) and appropriate initial parameters. We present SMASH to directly read off appropriate collision parameters directly from raw input video recordings. Technically we enable this by utilizing laws of rigid body collision to regularize the problem of lifting 2D trajectories to a physically valid 3D reconstruction of the collision. The reconstructed sequences can then be modified and combined to easily author novel and plausible collisions. We evaluate our system on a range of synthetic scenes and demonstrate the effectiveness of our method by accurately reconstructing several complex real world collision events.

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 "explainable machine learning for scientific insights and discoveries"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "a rate distortion framework for explaining neural network decisions"
    - "explaining explanations in ai"
    - "neural guided symbolic regression with semantic prior"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "causality for machine learning"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "the challenge of crafting intelligible intelligence"

Connected through hyperedge HE-3 (induced by source paper "unsupervised intuitive physics from past experiences"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "neural allocentric intuitive physics prediction from real videos"
    - "graph networks as learnable physics engines for inference and control"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "unsupervised intuitive physics from visual observations"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "recurrent environment simulators"

Connected through hyperedge HE-4 (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"
    - "learning to represent mechanics via long term extrapolation and interpolation"

Connected through hyperedge HE-5 (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 represent mechanics via long term extrapolation and interpolation"
    - "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"

Connected through hyperedge HE-6 (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"
    - "learning to represent mechanics via long term extrapolation and interpolation"

Connected through hyperedge HE-7 (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"
    - "learning to represent mechanics via long term extrapolation and interpolation"
    - "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"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "learning a physical long term predictor"):
  with members:
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "learning physical intuition of block towers by example"
    - "interaction networks for learning about objects relations and physics"
    - "visual stability prediction and its application to manipulation"
    - "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.GR(Graphics)