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: "high speed tracking with kernelized correlation filters"
  Abstract: The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.

Connected through hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation"):
  with members:
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "deep visual attention prediction"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"

Connected through hyperedge HE-3 (induced by source paper "multi drone based single object tracking with agent sharing network"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-4 (induced by source paper "keyfilter aware real time uav object tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "unsupervised deep tracking"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-5 (induced by source paper "rethinking temporal object detection from robotic perspectives"):
  with members:
    - "joint anchor feature refinement for real time accurate object detection in images and videos"
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "temporally identity aware ssd with attentional lstm"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-6 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking"):
  with members:
    - "multi modal fusion for end to end rgb t tracking"
    - "ranked list loss for deep metric learning"
    - "multi adapter rgbt tracking"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "fanet quality aware feature aggregation network for rgb t tracking"
    - "rgb t object tracking benchmark and baseline"

Connected through hyperedge HE-7 (induced by source paper "training set distillation for real time uav object tracking"):
  with members:
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "large margin object tracking with circulant feature maps"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "discriminative scale space tracking"

Connected through hyperedge HE-8 (induced by source paper "a bayesian filter for multi view 3d multi object tracking with occlusion handling"):
  with members:
    - "deep multi camera people detection"
    - "deep occlusion reasoning for multi camera multi target detection"
    - "yolo9000 better faster stronger"
    - "performance measures and a data set for multi target multi camera tracking"
    - "you only look once unified real time object detection"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "fast r cnn"
    - "motchallenge 2015 towards a benchmark for multi target 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)