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 "multi label zero shot learning with transfer aware label embedding projection"):
  Abstract: Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning. The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. Auxiliary information can be conveniently incorporated to guide the label embedding projection to further improve label relation structures for zero-shot knowledge transfer. We conduct experiments for zero-shot multi-label image classification. The results demonstrate the efficacy of the proposed approach.

Includes members:
  Member "multi label zero shot learning with structured knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bridging knowledge graphs to generate scene graphs")
  Member "recovering the missing link predicting class attribute associations for unsupervised zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "any shot object detection")
  Member "less is more zero shot learning from online textual documents with noise suppression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "fast zero shot image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "captioning images with novel objects via online vocabulary expansion")
  Member "cnn rnn a unified framework for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learn to predict sets using feed forward neural networks")
  Member "improving multi label learning with missing labels by structured semantic correlations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning a deep convnet for multi label classification with partial labels")
  Member "predicting visual exemplars of unseen classes for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning adaptive classifiers synthesis for generalized few shot learning")
  Member "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyperlearn a distributed approach for representation learning in datasets with many modalities")

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)