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 "outfit compatibility prediction and diagnosis with multi layered comparison network"):
  Abstract: Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities. However, there are few works explore how to explain the prediction, which limits the persuasiveness and effectiveness of the model. In this work, we propose an approach to not only predict but also diagnose the outfit compatibility. We introduce an end-to-end framework for this goal, which features for: (1) The overall compatibility is learned from all type-specified pairwise similarities between items, and the backpropagation gradients are used to diagnose the incompatible factors. (2) We leverage the hierarchy of CNN and compare the features at different layers to take into account the compatibilities of different aspects from the low level (such as color, texture) to the high level (such as style). To support the proposed method, we build a new type-specified outfit dataset named Polyvore-T based on Polyvore dataset. We compare our method with the prior state-of-the-art in two tasks: outfit compatibility prediction and fill-in-the-blank. Experiments show that our approach has advantages in both prediction performance and diagnosis ability.

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 "recommending outfits from personal closet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward explainable fashion recommendation")
  Member "outfit generation and style extraction via bidirectional lstm and autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning based co creative design framework")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "compatibility family learning for item recommendation and generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "style2vec representation learning for fashion items from style sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")

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)