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 "deep connections between learning from limited labels physical parameter estimation inspiration for regularization"):
  Abstract: Recently established equivalences between differential equations and the structure of neural networks enabled some interpretation of training of a neural network as partial-differential-equation (PDE) constrained optimization. We add to the previously established connections, explicit regularization that is particularly beneficial in the case of single large-scale examples with partial annotation. We show that explicit regularization of model parameters in PDE constrained optimization translates to regularization of the network output. Examination of the structure of the corresponding Lagrangian and backpropagation algorithm do not reveal additional computational challenges. A hyperspectral imaging example shows that minimum prior information together with cross-validation for optimal regularization parameters boosts the segmentation accuracy.

Includes members:
  Member "symmetric block low rank layers for fully reversible multilevel neural networks":
    also connected through other hyperedges:
      (none)
  Member "imexnet a forward stable deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dissecting neural odes")
  Member "fully hyperbolic convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "symmetric block low rank layers for fully reversible multilevel neural networks")
  Member "anodev2 a coupled neural ode evolution framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dissecting neural odes")
  Member "leanconvnets low cost yet effective convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "anode unconditionally accurate memory efficient gradients for neural odes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpolated adjoint method for neural odes")
  Member "regularization for deep learning a taxonomy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")

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)