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 "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences"):
  Abstract: Understanding the structure of complex activities in videos is one of the many challenges faced by action recognition methods. To overcome this challenge, not only do methods need a solid knowledge of the visual structure of underlying features but also a good interpretation of how they could change over time. Consequently, action segmentation tasks must take into account not only the visual cues from individual frames, but their characteristics as a temporal sequence of features. #R##N#This work presents our findings on the impact of incorporating both visual and temporal learning on an unsupervised action segmentation pipeline. We introduce a novel approach to extract relevant visual and temporal features from untrimmed sequences for the temporal localization of sub-activities within complex actions without any labeling information. Through extensive experimentation on two benchmark datasets -- Breakfast Actions, and YouTube Instructions -- we show that the proposed approach is able to provide a meaningful visual and temporal embedding from the visual cues from contiguous video frames and that it indeed helps in temporal segmentation.

Includes members:
  Member "a hybrid rnn hmm approach for weakly supervised temporal action segmentation":
    also connected through other hyperedges:
      (none)
  Member "self supervised visual feature learning with deep neural networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "weakly supervised energy based learning for action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "set constrained viterbi for set supervised action segmentation")
  Member "unsupervised learning of action classes with continuous temporal embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration")
  Member "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "set constrained viterbi for set supervised action segmentation")
  Member "unsupervised learning and segmentation of complex activities from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration")
  Member "neuralnetwork viterbi a framework for weakly supervised video learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "a perceptual prediction framework for self supervised event segmentation":
    also connected through other hyperedges:
      (none)

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)