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 context label embedding"):
  Abstract: Label embedding plays an important role in zero-shot learning. Side information such as attributes, semantic text representations, and label hierarchy are commonly used as the label embedding in zero-shot classification tasks. However, the label embedding used in former works considers either only one single context of the label, or multiple contexts without dependency. Therefore, different contexts of the label may not be well aligned in the embedding space to preserve the relatedness between labels, which will result in poor interpretability of the label embedding. In this paper, we propose a Multi-Context Label Embedding (MCLE) approach to incorporate multiple label contexts, e.g., label hierarchy and attributes, within a unified matrix factorization framework. To be specific, we model each single context by a matrix factorization formula and introduce a shared variable to capture the dependency among different contexts. Furthermore, we enforce sparsity constraint on our multi-context framework to strengthen the interpretability of the learned label embedding. Extensive experiments on two real-world datasets demonstrate the superiority of our MCLE in label description and zero-shot image classification.

Includes members:
  Member "adversarially regularized graph autoencoder for graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "attributed social network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crl class representative learning for image classification")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "zero shot learning by convex combination of semantic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)