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: "zero shot learning posed as a missing data problem"
  Abstract: This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way \--- our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. In experiments, our method outperforms the state-of-the-art on two popular datasets.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a large scale attribute dataset for zero shot learning"):
  with members:
    - "semi latent gan learning to generate and modify facial images from attributes"
    - "zero shot learning the good the bad and the ugly"
    - "invertible conditional gans for image editing"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "yolo9000 better faster stronger"
    - "conditional image generation with pixelcnn decoders"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "stacked semantic guided attention model for fine grained zero shot learning"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "transductive zero shot learning with a self training dictionary approach"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "semantically consistent regularization for zero shot recognition"

Connected through hyperedge HE-4 (induced by source paper "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"):
  with members:
    - "prototypical networks for few shot learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning a deep embedding model for zero shot learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view zero shot learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "ai challenger a large scale dataset for going deeper in image understanding"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "recent advances in zero shot recognition"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "image captioning with semantic attention"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "zero shot learning via class conditioned deep generative models"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning with generative latent prototype model"
    - "transductive zero shot learning with a self training dictionary approach"
    - "a simple exponential family framework for zero shot learning"
    - "learning robust visual semantic embeddings"
    - "deconvolutional latent variable model for text sequence matching"
    - "transductive zero shot learning with adaptive structural embedding"
    - "zero shot visual recognition via bidirectional latent embedding"

Connected through hyperedge HE-7 (induced by source paper "a simple exponential family framework for zero shot learning"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "improving semantic embedding consistency by metric learning for zero shot classification"
    - "learning joint feature adaptation for zero shot recognition"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-8 (induced by source paper "zero shot learning via shared reconstruction graph pursuit"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "very deep convolutional networks for large scale image recognition"
    - "zero shot learning by convex combination of semantic embeddings"
    - "zero shot learning through cross modal transfer"

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)