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 "semi supervised feature level attribute manipulation for fashion image retrieval"):
  Abstract: With a growing demand for the search by image, many works have studied the task of fashion instance-level image retrieval (FIR). Furthermore, the recent works introduce a concept of fashion attribute manipulation (FAM) which manipulates a specific attribute (e.g color) of a fashion item while maintaining the rest of the attributes (e.g shape, and pattern). In this way, users can search not only "the same" items but also "similar" items with the desired attributes. FAM is a challenging task in that the attributes are hard to define, and the unique characteristics of a query are hard to be preserved. Although both FIR and FAM are important in real-life applications, most of the previous studies have focused on only one of these problem. In this study, we aim to achieve competitive performance on both FIR and FAM. To do so, we propose a novel method that converts a query into a representation with the desired attributes. We introduce a new idea of attribute manipulation at the feature level, by matching the distribution of manipulated features with real features. In this fashion, the attribute manipulation can be done independently from learning a representation from the image. By introducing the feature-level attribute manipulation, the previous methods for FIR can perform attribute manipulation without sacrificing their retrieval performance.

Includes members:
  Member "combination of multiple global descriptors for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmark on tricks for large scale image retrieval")
  Member "learning type aware embeddings for fashion compatibility":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "hard aware point to set deep metric for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "informative sample mining network for multi domain image to image translation")
  Member "give me a hint navigating image databases using human in the loop feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning similarity conditions without explicit supervision")
  Member "began boundary equilibrium generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "learning to discover cross domain relations with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "leveraging weakly annotated data for fashion image retrieval and label prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noiserank unsupervised label noise reduction with dependence models")

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)