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 "histogram layers for texture analysis"):
  Abstract: We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed histogram layer leverages the spatial distribution of features for texture analysis and parameters for the layer are estimated during backpropagation. We compare our method with state-of-the-art texture encoding methods such as the Deep Encoding Network (DEP) and Deep Texture Encoding Network (DeepTEN) on three texture datasets: (1) the Describable Texture Dataset (DTD); (2) an extension of the ground terrain in outdoor scenes (GTOS-mobile); (3) and a subset of the Materials in Context (MINC-2500) dataset. Results indicate that the inclusion of the proposed histogram layer improves performance. The source code for the histogram layer is publicly available.

Includes members:
  Member "learnable histogram statistical context features for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable bow networks for adversarial example detection")
  Member "from bow to cnn two decades of texture representation for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "deep texture manifold for ground terrain recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes")
  Member "texture classification in extreme scale variations using ganet":
    also connected through other hyperedges:
      (none)
  Member "deep ten texture encoding network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization")
  Member "a theoretical analysis of deep neural networks for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "material recognition in the wild with the materials in context database":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

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.LG(Machine Learning)