human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visual physics discovering physical laws from videos"):
  Abstract: In this paper, we teach a machine to discover the laws of physics from video streams. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). We evaluate our ability to discover physical laws on videos of elementary physical phenomena, such as projectile motion or circular motion. These elementary tasks have textbook governing equations and enable ground truth verification of our approach.

Includes members:
  Member "data driven partial derivative equations discovery with evolutionary approach":
    also connected through other hyperedges:
      (none)
  Member "bounce and learn modeling scene dynamics with real world bounces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "physics based rendering for improving robustness to rain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nuscenes a multimodal dataset for autonomous driving")
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "neural guided symbolic regression with semantic prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable machine learning for scientific insights and discoveries")
  Member "physics based neural networks for shape from polarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blending diverse physical priors with neural networks")
  Member "heavy rain image restoration integrating physics model and conditional adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "physics guided machine learning for scientific discovery an application in simulating lake temperature profiles")
  Member "neural lander stable drone landing control using learned dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")

Question: Please tell me which class the source paper 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)