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 better baseline for ava"):
  Abstract: We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features - in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3 of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.

Includes members:
  Member "yh technologies at activitynet challenge 2018":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning spatio temporal representation with local and global diffusion")
  Member "learning spatio temporal representation with pseudo 3d residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "the kinetics human action video dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "ava a video dataset of spatio temporally localized atomic visual actions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatio temporal ranked attention networks for video captioning")

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)