human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dcfnet discriminant correlation filters network for visual tracking"
  Abstract: Discriminant Correlation Filters (DCF) based methods now become a kind of dominant approach to online object tracking. The features used in these methods, however, are either based on hand-crafted features like HoGs, or convolutional features trained independently from other tasks like image classification. In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously. Specifically, we treat DCF as a special correlation filter layer added in a Siamese network, and carefully derive the backpropagation through it by defining the network output as the probability heatmap of object location. Since the derivation is still carried out in Fourier frequency domain, the efficiency property of DCF is preserved. This enables our tracker to run at more than 60 FPS during test time, while achieving a significant accuracy gain compared with KCF using HoGs. Extensive evaluations on OTB-2013, OTB-2015, and VOT2015 benchmarks demonstrate that the proposed DCFNet tracker is competitive with several state-of-the-art trackers, while being more compact and much faster.

Connected through hyperedge HE-1 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-2 (induced by source paper "got 10k a large high diversity benchmark for generic object tracking in the wild"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "class balanced loss based on effective number of samples"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-3 (induced by source paper "real time correlation tracking via joint model compression and transfer"):
  with members:
    - "unsupervised deep tracking"
    - "real time mdnet"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "good features to correlate for visual tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "deep attentive tracking via reciprocative learning"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-4 (induced by source paper "high performance visual object tracking with unified convolutional networks"):
  with members:
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-5 (induced by source paper "joint task self supervised learning for temporal correspondence"):
  with members:
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "scops self supervised co part segmentation"
    - "deep clustering for unsupervised learning of visual features"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep tracking"):
  with members:
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "a twofold siamese network for real time object tracking"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "deep attentive tracking via reciprocative learning"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

Connected through hyperedge HE-7 (induced by source paper "synthetic data generation for end to end thermal infrared tracking"):
  with members:
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "end to end representation learning for correlation filter based tracking"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "real time visual tracking using spatial aware temporal aggregation network"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "efficient video object segmentation via network modulation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "flow guided feature aggregation for video object detection"

Question: Please tell me which class the center node 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)