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: "from zero shot learning to conventional supervised classification unseen visual data synthesis"
  Abstract: Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting sufficient images for ever-growing new classes is unattainable. In this paper, we propose a new Zero-shot learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage. Hereafter, ZSL recognition is converted into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical classifiers such as SVM. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data. Extensive experimental results suggest that our proposed approach significantly improve the state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "order matters shuffling sequence generation for video prediction"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "stochastic adversarial video prediction"
    - "robust cross view gait identification with evidence a discriminant gait gan diggan approach on 10000 people"
    - "learning to decompose and disentangle representations for video prediction"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "folded recurrent neural networks for future video prediction"

Connected through hyperedge HE-2 (induced by source paper "semantically tied paired cycle consistency for zero shot sketch based image retrieval"):
  with members:
    - "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-3 (induced by source paper "atzsl defensive zero shot recognition in the presence of adversaries"):
  with members:
    - "convolutional prototype learning for zero shot recognition"
    - "hierarchical prototype learning for zero shot recognition"
    - "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"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-4 (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"
    - "multi modal cycle consistent generalized zero shot learning"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "creativity inspired zero shot learning"):
  with members:
    - "design design inspiration from generative networks"
    - "multi modal cycle consistent generalized zero shot learning"
    - "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"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "context aware zero shot learning for object recognition"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "modular generative adversarial networks"
    - "deep contextualized word representations"
    - "context encoding for semantic segmentation"
    - "semantic autoencoder for zero shot learning"
    - "learning transferable architectures for scalable image recognition"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-7 (induced by source paper "dual adversarial semantics consistent network for generalized zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "zero shot kernel learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-8 (induced by source paper "stacked semantic guided network for zero shot sketch based image retrieval"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "semantic autoencoder for zero shot learning"
    - "deep sketch hashing fast free hand sketch based image retrieval"

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)