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 "domain specific embedding network for zero shot recognition"):
  Abstract: Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most existing methods directly adapt a well-trained projection from one domain to another, thereby ignoring the serious bias problem caused by domain differences. To address this issue, we propose a novel Domain-Specific Embedding Network (DSEN) that can apply specific projections to different domains for unbiased embedding, as well as several domain constraints. In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains. To prevent the two specific projections from breaking the semantic relationship, a semantic reconstruction constraint is proposed by applying the same decoder function to them in a cycle consistency way. Furthermore, a domain division constraint is developed to directly penalize the margin between real and pseudo image features in respective seen and unseen domains, which can enlarge the inter-domain difference of visual features. Extensive experiments on four public benchmarks demonstrate the effectiveness of DSEN with an average of $9.2\%$ improvement in terms of harmonic mean. The code is available in \url{this https URL}.

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 "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 "zero shot kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")
  Member "preserving semantic relations for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning")
  Member "transductive unbiased embedding for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "zero shot visual recognition using semantics preserving adversarial embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "crl class representative learning for image classification")
  Member "a generative model for zero shot learning using conditional variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning")

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)