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: "transductive zero shot learning with adaptive structural embedding"
  Abstract: Zero-shot learning (ZSL) endows the computer vision system with the inferential capability to recognize instances of a new category that has never seen before. Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively. To address both challenges, this paper presents two corresponding methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective Strategy (SPASS), respectively. Specifically, ASTE formulates the visualsemantic interactions in a latent structural SVM framework to adaptively adjust the slack variables to embody the different reliableness among training instances. In this way, the reliable instances are imposed with small punishments, wheras the less reliable instances are imposed with more severe punishments. Thus, it ensures a more discriminative embedding. On the other hand, SPASS offers a framework to alleviate the domain shift problem in ZSL, which exploits the unseen data in an easy to hard fashion. Particularly, SPASS borrows the idea from selfpaced learning by iteratively selecting the unseen instances from reliable to less reliable to gradually adapt the knowledge from the seen domain to the unseen domain. Subsequently, by combining SPASS and ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively reinforce the classification capacity. Extensive experiments on three benchmark datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and TASTE. Furthermore, we also propose a fast training (FT) strategy to improve the efficiency of most of existing ZSL methods. The FT strategy is surprisingly simple and general enough, which can speed up the training time of most existing methods by 4~300 times while holding the previous performance.

Connected through hyperedge HE-1 (induced by source paper "a zero shot based fingerprint presentation attack detection system"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "oct fingerprints resilience to presentation attacks"
    - "multi modal cycle consistent generalized zero shot learning"
    - "a zero shot framework for sketch based image retrieval"
    - "transductive unbiased embedding for zero shot learning"
    - "deepmasterprints generating masterprints for dictionary attacks via latent variable evolution"
    - "synthesized classifiers for zero shot learning"
    - "transductive multi view zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "transfer adaptation learning a decade survey"):
  with members:
    - "optimal projection guided transfer hashing for image retrieval"
    - "representation learning by rotating your faces"
    - "open set domain adaptation for image and action recognition"
    - "deep visual domain adaptation a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "load balanced gans for multi view face image synthesis"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "improving generalization via attribute selection on out of the box data"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "from zero shot learning to cold start recommendation"
    - "attribute guided network for cross modal zero shot hashing"
    - "complementary attributes a new clue to zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a domain based approach to social relation recognition"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "label embedding for image classification"

Connected through hyperedge HE-4 (induced by source paper "attribute guided network for cross modal zero shot hashing"):
  with members:
    - "transductive zero shot hashing via coarse to fine similarity mining"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "hashnet deep learning to hash by continuation"
    - "supervised matrix factorization for cross modality hashing"
    - "deep cross modal hashing"
    - "transductive multi view zero shot learning"
    - "hashing on nonlinear manifolds"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-5 (induced by source paper "multi label zero shot human action recognition via joint latent embedding"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "alternative semantic representations for zero shot human action recognition"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "zero shot learning the good the bad and the ugly"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"

Connected through hyperedge HE-6 (induced by source paper "zero shot learning via class conditioned deep generative models"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning with generative latent prototype model"
    - "transductive zero shot learning with a self training dictionary approach"
    - "a simple exponential family framework for zero shot learning"
    - "learning robust visual semantic embeddings"
    - "deconvolutional latent variable model for text sequence matching"
    - "zero shot learning posed as a missing data problem"
    - "zero shot visual recognition via bidirectional latent embedding"

Connected through hyperedge HE-7 (induced by source paper "zero shot learning via latent space encoding"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "transductive zero shot learning with a self training dictionary approach"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning a deep embedding model for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view 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)