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 "real time visual tracking using spatial aware temporal aggregation network"):
  Abstract: More powerful feature representations derived from deep neural networks benefit visual tracking algorithms widely. However, the lack of exploitation on temporal information prevents tracking algorithms from adapting to appearances changing or resisting to drift. This paper proposes a correlation filter based tracking method which aggregates historical features in a spatial-aligned and scale-aware paradigm. The features of historical frames are sampled and aggregated to search frame according to a pixel-level alignment module based on deformable convolutions. In addition, we also use a feature pyramid structure to handle motion estimation at different scales, and address the different demands on feature granularity between tracking losses and deformation offset learning. By this design, the tracker, named as Spatial-Aware Temporal Aggregation network (SATA), is able to assemble appearances and motion contexts of various scales in a time period, resulting in better performance compared to a single static image. Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical. Our code will be made publicly available at \href{this https URL}{this https URL}.

Includes members:
  Member "object detection in video with spatiotemporal sampling networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "efficient video object segmentation via network modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "lasot a high quality benchmark for large scale single object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "good features to correlate for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "unveiling the power of deep tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "vital visual tracking via adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "dcfnet discriminant correlation filters network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")

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)