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 "creating capsule wardrobes from fashion images"):
  Abstract: We propose to automatically create capsule wardrobes. Given an inventory of candidate garments and accessories, the algorithm must assemble a minimal set of items that provides maximal mix-and-match outfits. We pose the task as a subset selection problem. To permit efficient subset selection over the space of all outfit combinations, we develop submodular objective functions capturing the key ingredients of visual compatibility, versatility, and user-specific preference. Since adding garments to a capsule only expands its possible outfits, we devise an iterative approach to allow near-optimal submodular function maximization. Finally, we present an unsupervised approach to learn visual compatibility from "in the wild" full body outfit photos; the compatibility metric translates well to cleaner catalog photos and improves over existing methods. Our results on thousands of pieces from popular fashion websites show that automatic capsule creation has potential to mimic skilled fashionistas in assembling flexible wardrobes, while being significantly more scalable.

Includes members:
  Member "streetstyle exploring world wide clothing styles from millions of photos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fashion minimal edits for outfit improvement")
  Member "style2vec representation learning for fashion items from style sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks")
  Member "learning the latent look unsupervised discovery of a style coherent embedding from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "learning fashion compatibility with bidirectional lstms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "mining fashion outfit composition using an end to end deep learning approach on set data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "fashion forward forecasting visual style in fashion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "learning compatibility across categories for heterogeneous item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "multi task curriculum transfer deep learning of clothing attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pedestrian attribute recognition a survey")

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)