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 visual recognition via bidirectional latent embedding"
  Abstract: Zero-shot learning for visual recognition, e.g., object and action recognition, has recently attracted a lot of attention. However, it still remains challenging in bridging the semantic gap between visual features and their underlying semantics and transferring knowledge to semantic categories unseen during learning. Unlike most of the existing zero-shot visual recognition methods, we propose a stagewise bidirectional latent embedding framework to two subsequent learning stages for zero-shot visual recognition. In the bottom-up stage, a latent embedding space is first created by exploring the topological and labeling information underlying training data of known classes via a proper supervised subspace learning algorithm and the latent embedding of training data are used to form landmarks that guide embedding semantics underlying unseen classes into this learned latent space. In the top-down stage, semantic representations of unseen-class labels in a given label vocabulary are then embedded to the same latent space to preserve the semantic relatedness between all different classes via our proposed semi-supervised Sammon mapping with the guidance of landmarks. Thus, the resultant latent embedding space allows for predicting the label of a test instance with a simple nearest-neighbor rule. To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on four benchmark datasets in object and action recognition, i.e., AwA, CUB-200-2011, UCF101 and HMDB51. The experimental results under comparative studies demonstrate that our proposed approach yields the state-of-the-art performance under inductive and transductive settings.

Connected through hyperedge HE-1 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "human action recognition and prediction a survey"
    - "fine grained activity recognition in baseball videos"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"

Connected through hyperedge HE-4 (induced by source paper "tarn temporal attentive relation network for few shot and zero shot action recognition"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "prototypical networks for few shot learning"
    - "discriminative convolutional fisher vector network for action recognition"
    - "the kinetics human action video dataset"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "unsupervised human action detection by action matching"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-5 (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-6 (induced by source paper "generative model for zero shot sketch based image retrieval"):
  with members:
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "toward controlled generation of text"
    - "semantic autoencoder for zero shot learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "a simple exponential family framework for zero shot 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-7 (induced by source paper "unifying unsupervised domain adaptation and zero shot visual recognition"):
  with members:
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptation via structured prediction based selective pseudo labeling"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "domain symmetric networks for adversarial domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"

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)