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 "zstad zero shot temporal activity detection"):
  Abstract: An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities.

Includes members:
  Member "zero shot detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incremental few shot object detection")
  Member "zero shot object detection learning to simultaneously recognize and localize novel concepts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental few shot object detection")
  Member "towards universal representation for unseen action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crl class representative learning for image classification")
  Member "zero shot object detection by hybrid region embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "any shot object detection")
  Member "w talc weakly supervised temporal activity localization and classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "a unified approach for conventional zero shot generalized zero shot and few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "any shot object detection")
  Member "similarity r c3d for few shot temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised compositional featureaggregation for few shot recognition")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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)