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: "semi supervised zero shot learning by a clustering based approach"
  Abstract: In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. In this paper, we propose a novel semi-supervised zero-shot learning method that works on an embedding space corresponding to abstract deep visual features. We seek a linear transformation on signatures to map them onto the visual features, such that the mapped signatures of the seen classes are close to labeled samples of the corresponding classes and unlabeled data are also close to the mapped signatures of one of the unseen classes. #R##N#We use the idea that the rich deep visual features provide a representation space in which samples of each class are usually condensed in a cluster. The effectiveness of the proposed method is demonstrated through extensive experiments on four public benchmarks improving the state-of-the-art prediction accuracy on three of them.

Connected through hyperedge HE-1 (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"
    - "zero shot kernel learning"

Connected through hyperedge HE-2 (induced by source paper "semantic similarity based softmax classifier for zero shot learning"):
  with members:
    - "zero shot kernel learning"
    - "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"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"

Connected through hyperedge HE-3 (induced by source paper "joint concept matching space projection learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "joint dictionaries for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "zero and few shot learning with semantic feature synthesis and competitive learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "low shot learning from imaginary data"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "low shot learning with large scale diffusion"
    - "transductive zero shot learning with a self training dictionary approach"
    - "learning to learn meta critic networks for sample efficient learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "domain invariant projection learning for zero shot recognition"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "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"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "zero shot learning with sparse attribute propagation"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot kernel learning"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "learning from noisy web data with category level supervision"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "transductive zero shot learning with a self training dictionary approach"

Connected through hyperedge HE-7 (induced by source paper "fine grained classification using heterogeneous web data and auxiliary categories"):
  with members:
    - "zero shot kernel learning"
    - "learning from noisy web data with category level supervision"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "zero shot learning the good the bad and the ugly"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "places an image database for deep scene understanding"

Connected through hyperedge HE-8 (induced by source paper "transductive zero shot learning with adaptive structural embedding"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "synthesized classifiers for zero shot learning"
    - "convolution in convolution for network in network"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"

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)