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 high performance computing method for accelerating temporal action proposal generation"):
  Abstract: Temporal action recognition always depends on temporal action proposal generation to hypothesize actions. Applications require temporal action proposal generation to handle both large video dataset and generate more potential actions and suffer from high computation cost due to the bottleneck of temporal action proposal generation. To address this, we introduce a ring parallel architecture based on Message Passing Interface, which is a reliable communication protocol and could be supported by multiple programming languages. In our work, total data transmission is reduced by adding a connection between multiple computing load in our new architecture, which is different from the traditional Parameter Server architecture. Remarkably, our parallel architecture outperforms the Parameter Server architecture in the tasks of temporal action proposal generation, especially for large datasets of millions of videos. In addition, a time metric is proposed to evaluate the speed performance in the distributed training process.

Includes members:
  Member "aed net an abnormal event detection network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an efficient method of detection and recognition in remote sensing image based on multi angle region of interests")
  Member "bsn boundary sensitive network for temporal action proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "activitynet challenge 2017 summary":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constraining temporal relationship for action localization")
  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 "temporal context network for activity localization in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "turn tap temporal unit regression network for temporal action proposals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constraining temporal relationship for action localization")
  Member "temporal action localization in untrimmed videos via multi stage cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "convolutional two stream network fusion for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)