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 "weakly supervised action segmentation using mutual consistency"):
  Abstract: Action segmentation is the task of predicting the actions in each frame of a video. Because of the high cost of preparing training videos with full supervision for action segmentation, weakly supervised approaches which are able to learn only from transcripts are very appealing. In this paper, we propose a new approach for weakly supervised action segmentation based on a two branch network. The two branches of our network predict two redundant but different representations for action segmentation. During training we introduce a new mutual consistency loss (MuCon) that enforces that these two representations are consistent. Using MuCon and a transcript prediction loss, our network achieves state-of-the-art results for action segmentation and action alignment while being fully differentiable and faster to train since it does not require a costly alignment step during training.

Includes members:
  Member "ms tcn multi stage temporal convolutional network for action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "weakly supervised energy based learning for action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences")
  Member "large scale holistic video understanding":
    also connected through other hyperedges:
      (none)
  Member "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences")
  Member "group normalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "neuralnetwork viterbi a framework for weakly supervised video learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "spatio temporal channel correlation networks for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic image networks for human action recognition")
  Member "slowfast networks for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)