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: "correlation tracking via joint discrimination and reliability learning"
  Abstract: For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information. First, we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn the discrimination information between the target and backgrounds, and the reliability term encourages the final filter to focus on more reliable regions. Second, we introduce a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We conduct extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to evaluate the proposed tracker. Experimental results show that our tracker performs favorably against other state-of-the-art trackers.

Connected through hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "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"

Connected through hyperedge HE-2 (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-3 (induced by source paper "target aware deep tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "deep attentive tracking via reciprocative learning"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "learning the model update for siamese trackers"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "atom accurate tracking by overlap maximization"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-5 (induced by source paper "siamman siamese motion aware network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning discriminative model prediction for tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "real time mdnet"
    - "atom accurate tracking by overlap maximization"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"

Connected through hyperedge HE-7 (induced by source paper "skimming perusal tracking a framework for real time and robust long term tracking"):
  with members:
    - "gradnet gradient guided network for visual object tracking"
    - "real time mdnet"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-8 (induced by source paper "remove cosine window from correlation filter based visual trackers when and how"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"
    - "eco efficient convolution operators for tracking"

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)