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: "a generative adversarial approach for zero shot learning from noisy texts"
  Abstract: Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g.Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.

Connected through hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"

Connected through hyperedge HE-2 (induced by source paper "generative adversarial zero shot relational learning for knowledge graphs"):
  with members:
    - "cgans with projection discriminator"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "spectral normalization for generative adversarial networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "one shot relational learning for knowledge graphs"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "harnessing gans for zero shot learning of new classes in visual speech recognition"):
  with members:
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "end to end multi view lipreading"
    - "lipreading with long short term memory"
    - "matching networks for one shot learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

Connected through hyperedge HE-5 (induced by source paper "semantic graph convolutional networks for 3d human pose regression"):
  with members:
    - "cartoonish sketch based face editing in videos using identity deformation transfer"
    - "quantized densely connected u nets for efficient landmark localization"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "graph r cnn for scene graph generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "learning for new visual environments with limited labels"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "meta learning with latent embedding optimization"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "improved techniques for adversarial discriminative domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"

Connected through hyperedge HE-8 (induced by source paper "learning classifiers for domain adaptation zero and few shot recognition based on learning latent semantic parts"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "meta learning with latent embedding optimization"
    - "improving adversarial discriminative domain adaptation"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"

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)