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 recognition using dual visual semantic mapping paths"
  Abstract: Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.

Connected through hyperedge HE-1 (induced by source paper "recognizing part attributes with insufficient data"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "a large scale attribute dataset for zero shot learning"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "pseudo mask augmented object detection"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot learning with visual structure constraint"):
  with members:
    - "learning where to look semantic guided multi attention localization for zero shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "preserving semantic relations for zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-3 (induced by source paper "joint concept matching based learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "structured analysis dictionary learning for image classification"
    - "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"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "visual space optimization for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "domain invariant projection learning for zero shot recognition"
    - "generative dual adversarial network for generalized zero shot learning"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "mitigating the hubness problem for zero shot learning of 3d objects"):
  with members:
    - "zero shot learning of 3d point cloud objects"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "3dcapsule extending the capsule architecture to classify 3d point clouds"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (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"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-7 (induced by source paper "zero shot learning of 3d point cloud objects"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "multi label zero shot learning with structured knowledge graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-8 (induced by source paper "zero shot learning with the isoperimetric loss"):
  with members:
    - "zero shot kernel learning"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "learning to compare relation network for few shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "learning a deep embedding model for zero shot learning"
    - "graph learning from data under laplacian and structural constraints"

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)