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: "link the head to the beak zero shot learning from noisy text description at part precision"
  Abstract: In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like "beak" to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like "migrate" on classifier prediction. Images are encoded by a part-based CNN that detect bird parts and learn part-specific representation. Part-based visual classifiers are predicted from text descriptions of unseen visual classifiers to facilitate classification without training images (also known as zero-shot recognition). We performed our experiments on CUBirds 2011 dataset and improves the state-of-the-art text-based zero-shot recognition results from 34.7\% to 43.6\%. We also created large scale benchmarks on North American Bird Images augmented with text descriptions, where we also show that our approach outperforms existing methods. Our code, data, and models are publically available.

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"
    - "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"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "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-3 (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-4 (induced by source paper "rethinking zero shot learning a conditional visual classification perspective"):
  with members:
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "residual non local attention networks for image restoration"
    - "visual semantic reasoning for image text matching"
    - "multimodal style transfer via graph cuts"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "selective zero shot classification with augmented attributes"
    - "discriminative learning of latent features for zero shot recognition"
    - "support neighbor loss for person re identification"

Connected through hyperedge HE-5 (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-6 (induced by source paper "a semantics guided class imbalance learning model for zero shot classification"):
  with members:
    - "multi modal cycle consistent generalized zero shot learning"
    - "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"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "semantic guided multi attention localization for zero shot learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "transductive zero shot learning with visual structure constraint"
    - "moment matching for multi source domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"
    - "semantic autoencoder for zero shot learning"
    - "normface l 2 hypersphere embedding for face verification"
    - "prototypical networks for few shot learning"

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)