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 "zero shot learning posed as a missing data problem"):
  Abstract: This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way \--- our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. In experiments, our method outperforms the state-of-the-art on two popular datasets.

Includes members:
  Member "recovering the missing link predicting class attribute associations for unsupervised zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "synthesized classifiers for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "multi cue zero shot learning with strong supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "latent embeddings for zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "transductive multi view zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "evaluation of output embeddings for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")

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)