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 data driven reconstruction of physically valid collisions"
  Abstract: Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in real world can be difficult as one has to get timing and the object trajectories to be correctly synchronized. After trial-and-error iterations, when objects can actually be made to collide, then they are difficult to acquire 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 simply based on input video recordings. Specifically, we describe how to use laws of rigid body collision to regularize the problem of lifting 2D annotated poses to 3D reconstruction of collision sequences. The reconstructed sequences can then be modified and combined to easily author novel and plausible collision sequences. We demonstrate the system on various complex collision sequences.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sparse surface constraints for combining physics based elasticity simulation and correspondence free object reconstruction"):
  with members:
    - "nnwarp neural network based nonlinear deformation"
    - "neural material learning elastic constitutive material and damping models from sparse data"
    - "active animations of reduced deformable models with environment interactions"
    - "volumedeform real time volumetric non rigid reconstruction"

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)