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 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 "zeroshot 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 the semantic space and video space-time features of actions 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 improvements to the standard ZSL pipeline. First, we enhance significantly the semantic space mapping by proposing manifold-regularised regression and data augmentation strategies. Second, we evaluate two existing post processing strategies (transductive self-training and hubness correction), and show that they are complementary. We evaluate extensively our model on a wide range of human action datasets including HMDB51, UCF101, OlympicSports, CCV and TRECVID MED 13. The results demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance with a simple and efficient pipeline, and without supervised annotation of attributes.

Connected through hyperedge HE-1 (induced by source paper "zero shot visual recognition via bidirectional latent embedding"):
  with members:
    - "learning deep representations of fine grained visual descriptions"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "multi label zero shot learning via concept embedding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"

Connected through hyperedge HE-2 (induced by source paper "multi task zero shot action recognition with prioritised data augmentation"):
  with members:
    - "dynamic concept composition for zero example event detection"
    - "reasoning about linguistic regularities in word embeddings using matrix manifolds"
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "transductive zero shot action recognition by word vector embedding"
    - "improving zero shot learning by mitigating the hubness problem"

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)