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 "deep set to set matching and learning"):
  Abstract: Matching two sets of items, called set-to-set matching problem, is being recently raised. The difficulties of set-to-set matching over ordinary data matching lie in the exchangeability in 1) set-feature extraction and 2) set-matching score; the pair of sets and the items in each set should be exchangeable. In this paper, we propose a deep learning architecture for the set-to-set matching that overcomes the above difficulties, including two novel modules: 1) a cross-set transformation and 2) cross-similarity function. The former provides the exchangeable set-feature through interactions between two sets in intermediate layers, and the latter provides the exchangeable set matching through calculating the cross-feature similarity of items between two sets. We evaluate the methods through experiments with two industrial applications, fashion set recommendation, and group re-identification. Through these experiments, we show that the proposed methods perform better than a baseline given by an extension of the Set Transformer, the state-of-the-art set-input function.

Includes members:
  Member "group reidentification with multigrained matching and integration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "dressing as a whole outfit compatibility learning based on node wise graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "set transformer a framework for attention based permutation invariant neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot learning as domain adaptation algorithm and analysis")
  Member "learning type aware embeddings for fashion compatibility":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic latent residual video prediction")
  Member "recommending outfits from personal closet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward explainable fashion recommendation")
  Member "fashionnet personalized outfit recommendation with deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fashion outfit generation for e commerce")
  Member "outfit generation and style extraction via bidirectional lstm and autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning based co creative design framework")

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)