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 "using discriminative methods to learn fashion compatibility across datasets"):
  Abstract: Determining whether a pair of garments are compatible with each other is a challenging matching problem. Past works explored various embedding methods for learning such a relationship. This paper introduces using discriminative methods to learn compatibility, by formulating the task as a simple binary classification problem. We evaluate our approach using an established dataset of outfits created by non-experts and demonstrated an improvement of ~2.5% on established metrics over the state-of-the-art method. We introduce three new datasets of professionally curated outfits and show the consistent performance of our approach on expert-curated datasets. To facilitate comparing across outfit datasets, we propose a new metric which, unlike previously used metrics, is not biased by the average size of outfits. We also demonstrate that compatibility between two types of items can be query indirectly, and such query strategy yield improvements.

Includes members:
  Member "learning type aware embeddings for fashion compatibility":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "learning the latent look unsupervised discovery of a style coherent embedding from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "automatic attribute discovery with neural activations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aesthetic image captioning from weakly labelled photographs")
  Member "learning visual clothing style with heterogeneous dyadic co occurrences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare 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)