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 "constrained linear data feature mapping for image classification"):
  Abstract: In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet. From this viewpoint, we establish the detailed connections in a technical level between the traditional iterative schemes for constrained linear system and the architecture for the basic block of ResNet. Under these connections, we propose some natural modifications of ResNet type models which will have less parameters but can keep almost the same accuracy as these original models. Some numerical experiments are shown to demonstrate the validity of this constrained learning data-feature mapping assumption.

Includes members:
  Member "scan a scalable neural networks framework towards compact and efficient models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence")
  Member "mgnet a unified framework of multigrid and convolutional neural network":
    also connected through other hyperedges:
      (none)
  Member "learning neural pde solvers with convergence guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "optimization algorithm inspired deep neural network structure design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamical system inspired adaptive time stepping controller for residual network families")
  Member "a flow model of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mgnet a unified framework of multigrid and convolutional neural network")
  Member "learning across scales a multiscale method for convolution neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a derivative free method for solving elliptic partial differential equations with deep neural networks")
  Member "the reversible residual network backpropagation without storing activations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "beyond finite layer neural networks bridging deep architectures and numerical differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic latent residual video prediction")

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)