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 "simultaneously learning architectures and features of deep neural networks"):
  Abstract: This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explicitly enforces the optimizer to focus on promising candidate filters while suppressing contributions of less relevant ones. In the meanwhile, we further propose to enforce the diversities between filters and this diversity-based regularization term improves the trade-off between model sizes and accuracies. It turns out the interplay between architecture and feature optimizations improves the final compressed models, and the proposed method is compared favorably to existing methods, in terms of both models sizes and accuracies for a wide range of applications including image classification, image compression and audio classification.

Includes members:
  Member "interpretable convolutional filter pruning":
    also connected through other hyperedges:
      (none)
  Member "rethinking the value of network pruning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning efficient convolutional networks through network slimming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "data driven sparse structure selection for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "a survey of model compression and acceleration for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "learning structured sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "generalized backpropagation etude de cas orthogonality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "concept whitening for interpretable image recognition")
  Member "pruning filters for efficient convnets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)