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 "semantic image networks for human action recognition"):
  Abstract: In this paper, we propose the use of a semantic image, an improved representation for video analysis, principally in combination with Inception networks. The semantic image is obtained by applying localized sparse segmentation using global clustering prior to the approximate rank pooling, which summarizes the motion characteristics in single or multiple images. It incorporates the background information by overlaying a static background from the window onto the subsequent segmented frames. The idea is to improve the action–motion dynamics by focusing on the region, which is important for action recognition and encoding the temporal variances using the frame ranking method. We also propose the sequential combination of Inception-ResNetv2 and long–short-term memory network (LSTM) to leverage the temporal variances for improved recognition performance. Extensive analysis has been carried out on UCF101 and HMDB51 datasets, which are widely used in action recognition studies. We show that (1) the semantic image generates better activations and converges faster than its original variant, (2) using segmentation prior to approximate rank pooling yields better recognition performance, (3) the use of LSTM leverages the temporal variance information from approximate rank pooling to model the action behavior better than the base network, (4) the proposed representations are adaptive as they can be used with existing methods such as temporal segment and I3D ImageNet + Kinetics network to improve the recognition performance, and (5) the four-stream network architecture pre-trained on ImageNet + Kinetics and fine-tuned using the proposed representation achieves the state-of-the-art performance, 99.1% and 83.7% recognition accuracy on UCF101 and HMDB51, respectively.

Includes members:
  Member "spatiotemporal pyramid network for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modality compensation network cross modal adaptation for action recognition")
  Member "multi fiber networks for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "spatio temporal channel correlation networks for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine grained instance level sketch based video retrieval")
  Member "a 2 nets double attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object contextual representations for semantic segmentation")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a closer look at spatiotemporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "appearance and relation networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discriminative video representation learning using support vector classifiers")

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)