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: "end to end representation learning for correlation filter based tracking"
  Abstract: The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.

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 "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-3 (induced by source paper "os2d one stage one shot object detection by matching anchor features"):
  with members:
    - "ranked list loss for deep metric learning"
    - "one shot object detection with co attention and co excitation"
    - "one shot instance segmentation"
    - "neighbourhood consensus networks"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "lstd a low shot transfer detector for object detection"
    - "end to end weakly supervised semantic alignment"
    - "non local neural networks"

Connected through hyperedge HE-4 (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-5 (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-6 (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"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-7 (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-8 (induced by source paper "mpm joint representation of motion and position map for cell tracking"):
  with members:
    - "joint multi frame detection and segmentation for multi cell tracking"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "r cnns for pose estimation and action 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)