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: "multi hierarchical independent correlation filters for visual tracking"
  Abstract: For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information. In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT). The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion. Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal video. The multi-hierarchical deep features of CNN representing different semantic information can be fully excavated to track multi-scale objects. To better overcome the deep feature redundancy, each hierarchical features are independently fed into a single branch to implement the online learning of parameters. Finally, an adaptive weight scheme is integrated into the framework to fuse these independent multi-branch CFs for the better and more robust visual object tracking. Extensive experiments on OTB and VOT datasets show that the proposed MHIT tracker can significantly improve the tracking performance. Especially, it obtains a 20.1% relative performance gain compared to the top trackers on the VOT2017 challenge, and also achieves new state-of-the-art performance on the VOT2018 challenge.

Connected through hyperedge HE-1 (induced by source paper "high performance visual object tracking with unified convolutional networks"):
  with members:
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-2 (induced by source paper "state aware re identification feature for multi target multi camera tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "learning non uniform hypergraph for multi object tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "exploiting offset guided network for pose estimation and tracking"):
  with members:
    - "pose flow efficient online pose tracking"
    - "joint flow temporal flow fields for multi person tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "simple baselines for human pose estimation and tracking"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "uct learning unified convolutional networks for real time visual tracking"

Connected through hyperedge HE-4 (induced by source paper "visual coin tracking tracking of planar double sided objects"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "unveiling the power of deep tracking"
    - "youtube vos sequence to sequence video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "learning regression and verification networks for long term visual tracking"
    - "continual occlusions and optical flow estimation"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-5 (induced by source paper "motion control on bionic eyes a comprehensive review"):
  with members:
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"
    - "an efficient optical flow based motion detection method for non stationary scenes"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "end to end video level representation learning for action recognition"

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)