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 "snap and find deep discrete cross domain garment image retrieval"):
  Abstract: With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items. However, it is challenging for conventional retrieval systems to match up the item photos captured by customers and the ones officially released by stores, especially for garment images. To bridge the customer- and store- provided garment photos, existing studies have been widely exploiting the clothing attributes (\textit{e.g.,} black) and landmarks (\textit{e.g.,} collar) to learn a common embedding space for garment representations. Unfortunately they omit the sequential correlation of attributes and consume large quantity of human labors to label the landmarks. In this paper, we propose a deep multi-task cross-domain hashing termed \textit{DMCH}, in which cross-domain embedding and sequential attribute learning are modeled simultaneously. Sequential attribute learning not only provides the semantic guidance for embedding, but also generates rich attention on discriminative local details (\textit{e.g.,} black buttons) of clothing items without requiring extra landmark labels. This leads to promising performance and 306$\times$ boost on efficiency when compared with the state-of-the-art models, which is demonstrated through rigorous experiments on two public fashion datasets.

Includes members:
  Member "automatic spatially aware fashion concept discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")
  Member "cross domain image retrieval with attention modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "video captioning with transferred semantic attributes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "cp mtml coupled projection multi task metric learning for large scale face retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling strategies for gan synthetic data")
  Member "a survey on learning to hash":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "boosting image captioning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  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)