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 "learning structured semantic embeddings for visual recognition"):
  Abstract: Numerous embedding models have been recently explored to incorporate semantic knowledge into visual recognition. Existing methods typically focus on minimizing the distance between the corresponding images and texts in the embedding space but do not explicitly optimize the underlying structure. Our key observation is that modeling the pairwise image-image relationship improves the discrimination ability of the embedding model. In this paper, we propose the structured discriminative and difference constraints to learn visual-semantic embeddings. First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings. The discriminative constraints encourage separability for image instances of different classes. Second, we align the difference vector between a pair of image embeddings with that of the corresponding word embeddings. The difference constraints help regularize image embeddings to preserve the semantic relationships among word embeddings. Extensive evaluations demonstrate the effectiveness of the proposed structured embeddings for single-label classification, multi-label classification, and zero-shot recognition.

Includes members:
  Member "local similarity aware deep feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "informative sample mining network for multi domain image to image translation")
  Member "an empirical study and analysis of generalized zero shot learning for object recognition in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")
  Member "multi instance visual semantic embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval")
  Member "learning deep structure preserving image text embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "unsupervised learning of visual representations using videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "embedding label structures for fine grained feature representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")

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)