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: "bit biologically inspired tracker"
  Abstract: Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change, and occlusion. Given the superior tracking performance of human visual system (HVS), an ideal design of biologically inspired model is expected to improve computer visual tracking. This is, however, a difficult task due to the incomplete understanding of neurons’ working mechanism in the HVS. This paper aims to address this challenge based on the analysis of visual cognitive mechanism of the ventral stream in the visual cortex, which simulates shallow neurons (S1 units and C1 units) to extract low-level biologically inspired features for the target appearance and imitates an advanced learning mechanism (S2 units and C2 units) to combine generative and discriminative models for target location. In addition, fast Gabor approximation and fast Fourier transform are adopted for real-time learning and detection in this framework. Extensive experiments on large-scale benchmark data sets show that the proposed biologically inspired tracker performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness. The acceleration technique in particular ensures that biologically inspired tracker maintains a speed of approximately 45 frames/s.

Connected through hyperedge HE-1 (induced by source paper "state aware anti drift object tracking"):
  with members:
    - "fully convolutional siamese networks for object tracking"
    - "understanding and diagnosing visual tracking systems"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "staple complementary learners for real time tracking"

Connected through hyperedge HE-2 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-3 (induced by source paper "handcrafted and deep trackers a review of recent object tracking approaches"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "vital visual tracking via adversarial learning"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "deep attentive tracking via reciprocative learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vital visual tracking via adversarial learning"
    - "hierarchical attentive recurrent tracking"
    - "end to end active object tracking via reinforcement learning"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "crest convolutional residual learning for visual tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-5 (induced by source paper "high speed tracking with a fourier domain kernelized correlation filter"):
  with members:
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "end to end representation learning for correlation filter based tracking"
    - "dual deep network for visual tracking"
    - "on manifold preintegration for real time visual inertial odometry"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "real time visual tracking promoting the robustness of correlation filter learning"
    - "beyond correlation filters learning continuous convolution operators for visual 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)