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 "a perceptual prediction framework for self supervised event segmentation"):
  Abstract: Temporal segmentation of long videos is an important problem, that has largely been tackled through supervised learning, often requiring large amounts of annotated training data. In this paper, we tackle the problem of self-supervised temporal segmentation of long videos that alleviate the need for any supervision. We introduce a self-supervised, predictive learning framework that draws inspiration from cognitive psychology to segment long, visually complex videos into individual, stable segments that share the same semantics. We also introduce a new adaptive learning paradigm that helps reduce the effect of catastrophic forgetting in recurrent neural networks. Extensive experiments on three publicly available datasets - Breakfast Actions, 50 Salads, and INRIA Instructional Videos datasets show the efficacy of the proposed approach. We show that the proposed approach is able to outperform weakly-supervised and other unsupervised learning approaches by up to 24% and have competitive performance compared to fully supervised approaches. We also show that the proposed approach is able to learn highly discriminative features that help improve action recognition when used in a representation learning paradigm.

Includes members:
  Member "unsupervised learning and segmentation of complex activities from video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences")
  Member "weakly supervised action segmentation with iterative soft boundary assignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "predicting visual context for unsupervised event segmentation in continuous photo streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning event representations in image sequences by dynamic graph embedding")
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "connectionist temporal modeling for weakly supervised action labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "temporal convolutional networks for action segmentation and detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "bidirectional long short term memory for video description":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning")
  Member "segmental spatiotemporal cnns for fine grained action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel online action detection framework from untrimmed video streams")

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)