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 "learning features with differentiable closed form solver for tracking"):
  Abstract: We present a novel and easy-to-implement training framework for visual tracking. Our approach mainly focuses on learning feature embeddings in an end-to-end way, which can generalize well to the trackers based on online discriminatively trained ridge regression model. This goal is efficiently achieved by taking advantage of the following two important theories. 1) Ridge regression problem has closed-form solution and is implicit differentiation under the optimality condition. Therefore, its solver can be embedded as a layer with efficient forward and backward processes in training deep convolutional neural networks. 2) Woodbury identity can be utilized to ensure efficient solution of ridge regression problem when the high-dimensional feature embeddings are employed. Moreover, in order to address the extreme foreground-background class imbalance during training, we modify the origin shrinkage loss and then employ it as the loss function for efficient and effective training. It is worth mentioning that the above core parts of our proposed training framework are easy to be implemented with several lines of code under the current popular deep learning frameworks, thus our approach is easy to be followed. Extensive experiments on six public benchmarks, OTB2015, NFS, TrackingNet, GOT10k, VOT2018, and VOT2019, show that the proposed tracker achieves state-of-the-art performance, while running at over 30 FPS. Code will be made available.

Includes members:
  Member "learning discriminative model prediction for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "meta learning with differentiable convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "deeper and wider siamese networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "real time mdnet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking")
  Member "atom accurate tracking by overlap maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "siamese cascaded region proposal networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "distractor aware siamese networks for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "lasot a high quality benchmark for large scale single object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled 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)