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 "a semantics guided class imbalance learning model for zero shot classification"):
  Abstract: Zero-Shot Classification (ZSC) equips the learned model with the ability to recognize the visual instances from the novel classes via constructing the interactions between the visual and the semantic modalities. In contrast to the traditional image classification, ZSC is easily suffered from the class-imbalance issue since it is more concerned with the class-level knowledge transfer capability. In the real world, the class samples follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to be transferred to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot. Towards alleviating the class imbalance issue in ZSC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model. Specifically, we randomly select the same number of images from each class across all training classes to form a training batch to ensure that the sample-scarce classes contribute equally as those classes with sufficient samples during each iteration. Considering that the instances from the same class differ in class representativeness, we further develop an efficient semantics-guided feature fusion model to obtain discriminative class visual prototype for the following visual-semantic interaction process via distributing different weights to the selected samples based on their class representativeness. Extensive experiments on three imbalanced ZSC benchmark datasets for both the Traditional ZSC (TZSC) and the Generalized ZSC (GZSC) tasks demonstrate our approach achieves promising results especially for the unseen categories those are closely related to the sample-scarce seen categories.

Includes members:
  Member "multi modal cycle consistent generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "learning class prototypes via structure alignment for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning")
  Member "adaptive confidence smoothing for generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locality and compositionality in zero shot learning")
  Member "sch gan semi supervised cross modal hashing by generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal deep analysis for multimedia")
  Member "zero shot visual recognition using semantics preserving adversarial embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "semantic autoencoder for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crl class representative learning for image classification")
  Member "link the head to the beak zero shot learning from noisy text description at part precision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "a generative adversarial approach for zero shot learning from noisy texts":
    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)