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 "contrastive learning for lifted networks"):
  Abstract: In this work we address supervised learning of neural networks via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel hardware and assign energy models to discriminatively trained neural networks. We demonstrate that the training methods for lifted networks proposed in the literature have significant limitations and show how to use a contrastive loss to address those limitations. We demonstrate that this contrastive training approximates back-propagation in theory and in practice and that it is superior to the training objective regularly used for lifted networks.

Includes members:
  Member "lifted proximal operator machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentiable linearized admm")
  Member "fenchel lifted networks a lagrange relaxation of neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "lifted neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "proximal backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "training neural networks without gradients a scalable admm approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization")
  Member "equilibrium propagation bridging the gap between energy based models and backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning in memristive nanowire networks")
  Member "trusting svm for piecewise linear cnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective")

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)