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 "recommending outfits from personal closet"):
  Abstract: We consider grading a fashion outfit for recommendation, where we assume that users have a closet of items and we aim at producing a score for an arbitrary combination of items in the closet. The challenge in outfit grading is that the input to the system is a bag of item pictures that are unordered and vary in size. We build a deep neural network-based system that can take variable-length items and predict a score. We collect a large number of outfits from a popular fashion sharing website, Polyvore, and evaluate the performance of our grading system. We compare our model with a random-choice baseline, both on the traditional classification evaluation and on people's judgment using a crowdsourcing platform. With over 84% in classification accuracy and 91% matching ratio to human annotators, our model can reliably grade the quality of an outfit. We also build an outfit recommender on top of our grader to demonstrate the practical application of our model for a personal closet assistant.

Includes members:
  Member "pose guided person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations")
  Member "looking at outfit to parse clothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pose guided fashion image synthesis using deep generative model")
  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 "mining fashion outfit composition using an end to end deep learning approach on set data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "modeling visual compatibility through hierarchical mid level elements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "learning visual clothing style with heterogeneous dyadic co occurrences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "image based recommendations on styles and substitutes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "cross domain image retrieval with a dual attribute aware ranking network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")

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)