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: "learning discriminative model prediction for tracking"
  Abstract: The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, tracking requires the learning of a robust target-specific appearance model online, during the inference stage. To be end-to-end trainable, the online learning of the target model thus needs to be embedded in the tracking architecture itself. Due to these difficulties, the popular Siamese paradigm simply predicts a target feature template. However, such a model possesses limited discriminative power due to its inability of integrating background information. #R##N#We develop an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction. Our architecture is derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations. Furthermore, our approach is able to learn key aspects of the discriminative loss itself. The proposed tracker sets a new state-of-the-art on 6 tracking benchmarks, achieving an EAO score of 0.440 on VOT2018, while running at over 40 FPS.

Connected through hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "reppoints point set representation for object detection"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"
    - "deeper and wider siamese networks for real time visual tracking"

Connected through hyperedge HE-2 (induced by source paper "siamman siamese motion aware network for visual tracking"):
  with members:
    - "domainsiam domain aware siamese network for visual object tracking"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "fast visual object tracking with rotated bounding boxes"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"
    - "siamese cascaded region proposal networks for real time visual tracking"

Connected through hyperedge HE-3 (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "skimming perusal tracking a framework for real time and robust long term tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"

Connected through hyperedge HE-4 (induced by source paper "multi modal fusion for end to end rgb t tracking"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "rgb t object tracking benchmark and baseline"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-5 (induced by source paper "grounding tracking integration"):
  with members:
    - "zero shot grounding of objects from natural language queries"
    - "mask scoring r cnn"
    - "atom accurate tracking by overlap maximization"
    - "yolov3 an incremental improvement"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-6 (induced by source paper "learning features with differentiable closed form solver for tracking"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "atom accurate tracking by overlap maximization"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "dal a deep depth aware long term tracker"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "atom accurate tracking by overlap maximization"
    - "depth masked discriminative correlation filter"
    - "depth aware cnn for rgb d segmentation"
    - "distractor aware siamese networks for visual object tracking"
    - "object tracking by reconstruction with view specific discriminative correlation filters"
    - "acquisition of localization confidence for accurate object detection"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-8 (induced by source paper "discriminative and robust online learning for siamese visual tracking"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "atom accurate tracking by overlap maximization"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"

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)