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: "generative dual adversarial network for generalized zero shot learning"
  Abstract: This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem. In this paper, we propose a novel model that provides a unified framework for three different approaches: visual-> semantic mapping, semantic->visual mapping, and metric learning. Specifically, our proposed model consists of a feature generator that can generate various visual features given class embeddings as input, a regressor that maps each visual feature back to its corresponding class embedding, and a discriminator that learns to evaluate the closeness of an image feature and a class embedding. All three components are trained under the combination of cyclic consistency loss and dual adversarial loss. Experimental results show that our model not only preserves higher accuracy in classifying images from seen classes, but also performs better than existing state-of-the-art models in in classifying images from unseen classes.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "gtnet generative transfer network for zero shot object detection"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "visual data synthesis via gan for zero shot video classification"
    - "semantic autoencoder for zero shot learning"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "robust classification with convolutional prototype learning"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "deep anomaly detection with outlier exposure"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "preserving semantic relations for zero shot learning"
    - "generalized zero shot recognition based on visually semantic embedding"

Connected through hyperedge HE-4 (induced by source paper "learning for new visual environments with limited labels"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "meta learning with latent embedding optimization"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "improved techniques for adversarial discriminative domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"

Connected through hyperedge HE-5 (induced by source paper "learning classifiers for domain adaptation zero and few shot recognition based on learning latent semantic parts"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "meta learning with latent embedding optimization"
    - "improving adversarial discriminative domain adaptation"
    - "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"
    - "learning embedding adaptation for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "tgg transferable graph generation for zero shot and few shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "learning deep generative models of graphs"
    - "visual data synthesis via gan for zero shot video classification"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "a generative model for zero shot learning using conditional variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "dont even look once synthesizing features for zero shot detection"):
  with members:
    - "probabilistic semantic retrieval for surveillance videos with activity graphs"
    - "transferable contrastive network for generalized zero shot learning"
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"

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)