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 kernel learning"
  Abstract: In this paper, we address an open problem of zero-shot learning. Its principle is based on learning a mapping that associates feature vectors extracted from i.e. images and attribute vectors that describe objects and/or scenes of interest. In turns, this allows classifying unseen object classes and/or scenes by matching feature vectors via mapping to a newly defined attribute vector describing a new class. Due to importance of such a learning task, there exist many methods that learn semantic, probabilistic, linear or piece-wise linear mappings. In contrast, we apply well-established kernel methods to learn a non-linear mapping between the feature and attribute spaces. We propose an easy learning objective inspired by the Linear Discriminant Analysis, Kernel-Target Alignment and Kernel Polarization methods that promotes incoherence. We evaluate performance of our algorithm on the Polynomial as well as shift-invariant Gaussian and Cauchy kernels. Despite simplicity of our approach, we obtain state-of-the-art results on several zero-shot learning datasets and benchmarks including a recent AWA2 dataset.

Connected through hyperedge HE-1 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "semantically consistent regularization for zero shot recognition"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "moments in time dataset one million videos for event understanding"

Connected through hyperedge HE-3 (induced by source paper "rethinking class relations absolute relative few shot learning"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "how to train your maml"
    - "power normalizing second order similarity network for few shot learning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"

Connected through hyperedge HE-5 (induced by source paper "visual and semantic prototypes jointly guided cnn for generalized zero shot learning"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "reducing network agnostophobia"
    - "robust classification with convolutional prototype learning"
    - "towards effective deep embedding for zero shot learning"
    - "bi adversarial auto encoder for zero shot learning"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "semantic similarity based softmax classifier for zero shot learning"):
  with members:
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "semi supervised zero shot learning by a clustering based approach"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"

Connected through hyperedge HE-7 (induced by source paper "domain specific embedding network for zero shot recognition"):
  with members:
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "preserving semantic relations for zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-8 (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 visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "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"

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)