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 action recognition by word vector embedding"
  Abstract: The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL). To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute-based semantic representations. In this work, we explore word-vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic strategies to improve upon the standard ZSL pipeline. Most of these strategies are transductive in nature which means access to testing data in the training phase.

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 "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"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "stacked adversarial network for zero shot sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "generative model for zero shot sketch based image retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-4 (induced by source paper "zero shot activity recognition with videos"):
  with members:
    - "human action recognition and prediction a survey"
    - "unseen action recognition with multimodal learning"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint event detection and description in continuous video streams"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "zero shot activity recognition with verb attribute induction"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (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-6 (induced by source paper "action recognition in untrimmed videos with composite self attention two stream framework"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "end to end learning of motion representation for video understanding"
    - "towards universal representation for unseen action recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)