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 "supervised deep sparse coding networks"):
  Abstract: In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding. The SCN is built upon a number of cascading bottleneck modules, where each module consists of two sparse coding layers with relatively wide and slim dictionaries that are specialized to produce high dimensional discriminative features and low dimensional representations for clustering, respectively. During training, both the dictionaries and regularization parameters are optimized with an end-to-end supervised learning algorithm based on multilevel optimization. Effectiveness of an SCN with seven bottleneck modules is verified on several popular benchmark datasets. Remarkably, with few parameters to learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10 and CIFAR-100, respectively.

Includes members:
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep ten texture encoding network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "histogram layers for texture analysis")
  Member "augmenting supervised neural networks with unsupervised objectives for large scale image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks")
  Member "wide residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "swapout learning an ensemble of deep architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mothernets rapid deep ensemble learning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)