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 "structure fusion and propagation for zero shot learning"):
  Abstract: The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes). Existing ZSL methods usually construct the compatibility function between images and class labels with consideration of the relevance on the semantic classes (the manifold structure of semantic classes). However, the relationship of image classes (the manifold structure of image classes) is also very important for the compatibility model construction. It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL. To complement each other between the manifold structure of image classes and that of semantic classes information, we propose structure fusion and propagation (SFP) for improving the performance of ZSL for classification. SFP can jointly consider the manifold structure of image classes and that of semantic classes for approximating to the intrinsic structure of object classes. Moreover, the SFP can describe the constraint condition between the compatibility function and these manifold structures for balancing the influence of the structure fusion and propagation iteration. The SFP solution provides not only unseen class labels but also the relationship of two manifold structures that encodes the positive transfer in structure fusion and propagation. Experiments demonstrate that SFP can attain the promising results on the AwA, CUB, Dogs and SUN datasets.

Includes members:
  Member "discriminative learning of latent features for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an optimal transport framework for zero shot learning")
  Member "visual data synthesis via gan for zero shot video classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "joint intermodal and intramodal label transfers for extremely rare or unseen classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive locality preserving regression")
  Member "transductive zero shot learning with a self training dictionary approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer adaptation learning a decade survey")
  Member "zero shot recognition using dual visual semantic mapping paths":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recognizing part attributes with insufficient data")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crl class representative learning for image classification")
  Member "label embedding for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crl class representative learning for image classification")
  Member "synthesized classifiers for zero shot learning":
    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)