human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi modal cycle consistent generalized zero shot learning"
  Abstract: In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes. Current methods address GZSL by learning a transformation from the visual to the semantic space, exploring the assumption that the distribution of classes in the semantic and visual spaces is relatively similar. Such methods tend to transform unseen testing visual representations into one of the seen classes' semantic features instead of the semantic features of the correct unseen class, resulting in low accuracy GZSL classification. Recently, generative adversarial networks (GAN) have been explored to synthesize visual representations of the unseen classes from their semantic features - the synthesized representations of the seen and unseen classes are then used to train the GZSL classifier. This approach has been shown to boost GZSL classification accuracy, however, there is no guarantee that synthetic visual representations can generate back their semantic feature in a multi-modal cycle-consistent manner. This constraint can result in synthetic visual representations that do not represent well their semantic features. In this paper, we propose the use of such constraint based on a new regularization for the GAN training that forces the generated visual features to reconstruct their original semantic features. Once our model is trained with this multi-modal cycle-consistent semantic compatibility, we can then synthesize more representative visual representations for the seen and, more importantly, for the unseen classes. Our proposed approach shows the best GZSL classification results in the field in several publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "a zero shot based fingerprint presentation attack detection system"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "oct fingerprints resilience to presentation attacks"
    - "a zero shot framework for sketch based image retrieval"
    - "transductive unbiased embedding for zero shot learning"
    - "transductive zero shot learning with adaptive structural embedding"
    - "deepmasterprints generating masterprints for dictionary attacks via latent variable evolution"
    - "synthesized classifiers for zero shot learning"
    - "transductive multi view zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "f vaegan d2 a feature generating framework for any shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "spectral normalization for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "learning feature to feature translator by alternating back propagation for generative zero shot learning"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "learning where to look semantic guided multi attention localization for zero shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning to forecast and refine residual motion for image to video generation"
    - "cu net coupled u nets"
    - "cr gan learning complete representations for multi view generation"
    - "learning descriptor networks for 3d shape synthesis and analysis"

Connected through hyperedge HE-5 (induced by source paper "a semantics guided class imbalance learning model for zero shot classification"):
  with members:
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "adaptive confidence smoothing for generalized zero shot learning"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "task aware deep sampling for feature generation"):
  with members:
    - "tafe net task aware feature embeddings for low shot learning"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "a style based generator architecture for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "convolutional prototype learning for zero shot recognition"):
  with members:
    - "classifier and exemplar synthesis for zero shot learning"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "discriminative learning of latent features for zero shot recognition"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "preserving semantic relations for zero shot learning"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "creativity inspired zero shot learning"):
  with members:
    - "design design inspiration from generative networks"
    - "semantic autoencoder for zero shot learning"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "learning robust visual semantic embeddings"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "improved training of wasserstein gans"

Question: Please tell me which class the center node 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)