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 with iterative soft boundary assignment"):
  Abstract: In this work, we address the task of weakly-supervised human action segmentation in long, untrimmed videos. Recent methods have relied on expensive learning models, such as Recurrent Neural Networks (RNN) and Hidden Markov Models (HMM). However, these methods suffer from expensive computational cost, thus are unable to be deployed in large scale. To overcome the limitations, the keys to our design are efficiency and scalability. We propose a novel action modeling framework, which consists of a new temporal convolutional network, named Temporal Convolutional Feature Pyramid Network (TCFPN), for predicting frame-wise action labels, and a novel training strategy for weakly-supervised sequence modeling, named Iterative Soft Boundary Assignment (ISBA), to align action sequences and update the network in an iterative fashion. The proposed framework is evaluated on two benchmark datasets, Breakfast and Hollywood Extended, with four different evaluation metrics. Extensive experimental results show that our methods achieve competitive or superior performance to state-of-the-art methods.

Includes members:
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "dense captioning events in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dense video captioning")
  Member "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "towards automatic learning of procedures from web instructional videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "localizing moments in video with natural language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "connectionist temporal modeling for weakly supervised action labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "weakly supervised learning of actions from transcripts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")

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)