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 localization by sparse temporal pooling network"):
  Abstract: We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks. Our algorithm predicts temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. This objective is achieved by proposing a novel deep neural network that recognizes actions and identifies a sparse set of key segments associated with the actions through adaptive temporal pooling of video segments. We design the loss function of the network to comprise two terms--one for classification error and the other for sparsity of the selected segments. After recognizing actions with sparse attention weights for key segments, we extract temporal proposals for actions using temporal class activation mappings to estimate time intervals that localize target actions. The proposed algorithm attains state-of-the-art accuracy on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with weak supervision.

Includes members:
  Member "spatiotemporal pyramid network for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition")
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "untrimmednets for weakly supervised action recognition and detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "actionvlad learning spatio temporal aggregation for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "temporal action localization by structured maximal sums":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sf net single frame supervision for temporal action localization")

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)