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: "once for all a two flow convolutional neural network for visual tracking"
  Abstract: One of the main challenges of visual object tracking comes from the arbitrary appearance of objects. Most existing algorithms try to resolve this problem as an object-specific task, i.e., the model is trained to regenerate or classify a specific object. As a result, the model need to be initialized and retrained for different objects. In this paper, we propose a more generic approach utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN takes two inputs (one is object image patch, the other is search image patch), then outputs a response map which predicts how likely the object appears in a specific location. Unlike those object-specific approach, the YCNN is trained to measure the similarity between two image patches. Thus it will not be confined to any specific object. Furthermore the network can be end-to-end trained to extract both shallow and deep convolutional features which are dedicated for visual tracking. And once properly trained, the YCNN can be applied to track all kinds of objects without further training and updating. Benefiting from the once-for-all model, our algorithm is able to run at a very high speed of 45 frames-per-second. The experiments on 51 sequences also show that our algorithm achieves an outstanding performance.

Connected through hyperedge HE-1 (induced by source paper "generic multiview visual tracking"):
  with members:
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "learning spatial aware regressions for visual tracking"
    - "end to end flow correlation tracking with spatial temporal attention"

Connected through hyperedge HE-2 (induced by source paper "end to end feature fusion siamese network for adaptive visual tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "discriminative scale space tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "learning spatially regularized correlation filters for visual tracking"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a comparison of cnn based face and head detectors for real time video surveillance applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "bi directional graph structure information model for multi person pose estimation"):
  with members:
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "object detection from video tubelets with convolutional neural networks"
    - "structured feature learning for pose estimation"

Connected through hyperedge HE-7 (induced by source paper "target driven instance detection"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "a dataset for developing and benchmarking active vision"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "synthesizing training data for object detection in indoor scenes"
    - "fully convolutional siamese networks for object tracking"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "correlation filter selection for visual tracking using reinforcement learning"):
  with members:
    - "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"
    - "end to end active object tracking via reinforcement learning"
    - "large margin object tracking with circulant feature maps"
    - "tree structured reinforcement learning for sequential object localization"
    - "deep reinforcement learning for visual object tracking in videos"

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)