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 "dazsl dynamic attributes for zero shot learning"):
  Abstract: Inspired by earlier applications to still images, zero-shot activity recognition has largely focused on image-derived representations without regard to the video's temporal aspect. Since these methods cannot capture the time evolution of an activity, reversible actions such as entering and exiting a car are often indistinguishable. In this work, we present a simple and elegant framework for modeling activities using dynamic attribute signatures. We show that specifying temporal structure greatly increases zero-shot systems' discriminative power. We also extend our method to form the first framework to our knowledge for zero-shot joint segmentation and classification of activities in videos. We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also demonstrate the first results in zero-shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can even eliminate the need to train attribute detectors by using off-the-shelf object detectors to recognize activities in challenging security footage.

Includes members:
  Member "out of distribution detection for generalized zero shot action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "zero shot action recognition in videos a survey":
    also connected through other hyperedges:
      (none)
  Member "action2vec a crossmodal embedding approach to action learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "informed democracy voting based novelty detection for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "visual data synthesis via gan for zero shot video classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "tsm temporal shift module for efficient video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "cross modal and hierarchical modeling of video and text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "r c3d region convolutional 3d network for temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised temporal action localization using deep metric learning")

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)