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 "texture retrieval in the wild through detection based attributes"):
  Abstract: Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest). In this paper we show that a texel-based representation fits well with this task. In particular, we refer to Texel-Att, a recent texel-based descriptor which has shown to capture fine grained variations of a texture, for retrieval purposes. After a brief explanation of Texel-Att, we will show in our experiments that this descriptor is robust to distortions resulting from acquisitions in the wild by setting up an experiment in which textures from the ElBa (an Element-Based texture dataset) are artificially distorted and then used to retrieve the original image. We compare our approach with existing descriptors using a simple ranking framework based on distance functions. Results show that even under extreme conditions (such as a down-sampling with a factor of 10), we perform better than alternative approaches.

Includes members:
  Member "texel att representing and classifying element based textures by attributes":
    also connected through other hyperedges:
      (none)
  Member "a survey of recent advances in texture representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "texel att representing and classifying element based textures by attributes")
  Member "training deep networks with synthetic data bridging the reality gap by domain randomization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "looking beyond appearances synthetic training data for deep cnns in re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning multi human optical flow")
  Member "visual search at ebay":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised adversarial attacks on deep feature based retrieval with gan")
  Member "vnect real time 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "whittlesearch interactive image search with relative attribute feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dressing for diverse body shapes")

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)