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 "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  Abstract: In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.

Includes members:
  Member "a spatial layout and scale invariant feature representation for indoor scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image super resolution as a defense against adversarial attacks")
  Member "a discriminative representation of convolutional features for indoor scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tag based semantic features for scene image classification")
  Member "inverting visual representations with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable cnns for object classification")
  Member "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plug and play rescaling based crowd counting in static images")
  Member "natural scene recognition based on superpixels and deep boltzmann machines":
    also connected through other hyperedges:
      (none)
  Member "max margin based discriminative feature learning":
    also connected through other hyperedges:
      (none)
  Member "unsupervised feature learning with c svddnet":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised visual representations for cross modal retrieval")
  Member "multi scale orderless pooling of deep convolutional activation features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")

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)