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: "robust and real time deep tracking via multi scale domain adaptation"
  Abstract: Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Some new deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease on precision. In this paper, we propose to transfer the feature for image classification to the visual tracking domain via convolutional channel reductions. The channel reduction could be simply viewed as an additional convolutional layer with the specific task. It not only extracts useful information for object tracking but also significantly increases the tracking speed. To better accommodate the useful feature of the target in different scales, the adaptation filters are designed with different sizes. The yielded visual tracker is real-time and also illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos.

Connected through hyperedge HE-1 (induced by source paper "rethinking convolutional features in correlation filter based tracking"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "deep attentive tracking via reciprocative learning"
    - "learning spatial aware regressions for visual tracking"
    - "dual deep network for visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "sanet structure aware network for visual tracking"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-2 (induced by source paper "online visual robot tracking and identification using deep lstm networks"):
  with members:
    - "a monocular vision system for playing soccer in low color information environments"
    - "real time visual tracking and identification for a team of homogeneous humanoid robots"
    - "child sized 3d printed igus humanoid open platform"
    - "lstm a search space odyssey"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "globally consistent multi people tracking using motion patterns"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning spatial temporal regularized correlation filters for visual tracking"):
  with members:
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "visual object tracking performance measures revisited"
    - "fully convolutional siamese networks for object tracking"
    - "learning support correlation filters 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)