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: "predicting visual exemplars of unseen classes for zero shot learning"
  Abstract: Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations must be predictive of the locations of their corresponding visual exemplars. To this end, this reduces to training multiple kernel-based regressors from semantic representation-exemplar pairs from labeled data of the seen object categories. Despite its simplicity, our approach significantly outperforms existing zero-shot learning methods on standard benchmark datasets, including the ImageNet dataset with more than 20,000 unseen categories.

Connected through hyperedge HE-1 (induced by source paper "learning adaptive classifiers synthesis for generalized few shot learning"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "a closer look at few shot classification"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (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-3 (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"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-4 (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-5 (induced by source paper "transferable contrastive network for generalized zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "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"
    - "learning to compare relation network for few shot learning"
    - "semantically consistent regularization for zero shot recognition"

Connected through hyperedge HE-6 (induced by source paper "hierarchical prototype learning for zero shot recognition"):
  with members:
    - "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"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "few shot learning with global class representations"):
  with members:
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"

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)