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 "trained model fusion for object detection using gating network"):
  Abstract: The major approaches of transfer learning in computer vision have tried to adapt the source domain to the target domain one-to-one. However, this scenario is difficult to apply to real applications such as video surveillance systems. As those systems have many cameras installed at each location regarded as source domains, it is difficult to identify the proper source domain. In this paper, we introduce a new transfer learning scenario that has various source domains and one target domain, assuming video surveillance system integration. Also, we propose a novel method for automatically producing a high accuracy model by fusing models trained at various source domains. In particular, we show how to apply a gating network to fuse source domains for object detection tasks, which is a new approach. We demonstrate the effectiveness of our method through experiments on traffic surveillance datasets.

Includes members:
  Member "adaptive weighting multi field of view cnn for semantic segmentation in pathology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "visual saliency prediction using a mixture of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a dilated inception network for visual saliency prediction")
  Member "partial adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "edf ensemble distill and fuse for easy video labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self training with noisy student improves imagenet classification")
  Member "robust deep multi modal learning based on gated information fusion network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on single source robustness in deep fusion models")
  Member "rank of experts detection network ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predictive ensemble learning with application to scene text detection")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)