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 "non local netvlad encoding for video classification"):
  Abstract: This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides pre-extracted visual and audio features instead of raw videos. In this challenge, the submitted model is restricted to 1GB, which encourages participants focus on constructing one powerful single model rather than incorporating of the results from a bunch of models. Our system fuses six different sub-models into one single computational graph, which are categorized into three families. More specifically, the most effective family is the model with non-local operations following the NetVLAD encoding. The other two family models are Soft-BoF and GRU, respectively. In order to further boost single models performance, the model parameters of different checkpoints are averaged. Experimental results demonstrate that our proposed system can effectively perform the video classification task, achieving 0.88763 on the public test set and 0.88704 on the private set in terms of GAP@20, respectively. We finally ranked at the fourth place in the YouTube-8M video understanding challenge.

Includes members:
  Member "qiniu submission to activitynet challenge 2018":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ai enabling technologies a survey")
  Member "moments in time dataset one million videos for event understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolving losses for unsupervised video representation learning")
  Member "fine grained video attractiveness prediction using multimodal deep learning on a large real world dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interactive variance attention based online spoiler detection for time sync comments")
  Member "bidirectional attentive fusion with context gating for dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "video re localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatio temporal video re localization by warp lstm")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "averaging weights leads to wider optima and better generalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "learning spatio temporal representation with pseudo 3d residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")

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)