human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "zero shot action recognition in videos a survey"):
  Abstract: Zero-Shot Action Recognition has attracted attention in the last years, and many approaches have been proposed for recognition of objects, events, and actions in images and videos. There is a demand for methods that can classify instances from classes that are not present in the training of models, especially in the complex task of automatic video understanding, since collecting, annotating, and labeling videos are difficult and laborious tasks. We identify that there are many methods available in the literature, however, it is difficult to categorize which techniques can be considered state of the art. Despite the existence of some surveys about zero-shot action recognition in still images and experimental protocol, there is no work focusing on videos. Hence, in this paper, we present a survey of the methods comprising techniques to perform visual feature extraction and semantic feature extraction as well to learn the mapping between these features considering specifically zero-shot action recognition in videos. We also provide a complete description of datasets, experiments, and protocols, presenting open issues and directions for future work essential for the development of the computer vision research field.

Includes members:
  Member "action2vec a crossmodal embedding approach to action learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "fine grained activity recognition in baseball videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable grammars for videos")
  Member "towards universal representation for unseen action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "a short note about kinetics 600":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "graph convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta embeddings based on self attention")
  Member "visual data synthesis via gan for zero shot video classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "cross modal and hierarchical modeling of video and text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")

Question: Please tell me which class the source paper 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)