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: "towards a better match in siamese network based visual object tracker"
  Abstract: Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance. Despite the great success, this tracking framework still suffers from several limitations. First, it cannot properly handle large object rotation. Second, tracking gets easily distracted when the background contains salient objects. In this paper, we propose two simple yet effective mechanisms, namely angle estimation and spatial masking, to address these issues. The objective is to extract more representative features so that a better match can be obtained between the same object from different frames. The resulting tracker, named Siam-BM, not only significantly improves the tracking performance, but more importantly maintains the realtime capability. Evaluations on the VOT2017 dataset show that Siam-BM achieves an EAO of 0.335, which makes it the best-performing realtime tracker to date.

Connected through hyperedge HE-1 (induced by source paper "spm tracker series parallel matching for real time visual object tracking"):
  with members:
    - "real time mdnet"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "prediction tracking segmentation"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "fast online object tracking and segmentation a unifying approach"
    - "camera placement based on vehicle traffic for better city security surveillance"

Connected through hyperedge HE-3 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks"):
  with members:
    - "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "fast online object tracking and segmentation a unifying approach"
    - "learning dynamic memory networks for object tracking"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-4 (induced by source paper "discriminative and robust online learning for siamese visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "atom accurate tracking by overlap maximization"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-5 (induced by source paper "fast visual object tracking with rotated bounding boxes"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "human detection and tracking for video surveillance a cognitive science approach"
    - "fully convolutional siamese networks for object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "online object tracking with proposal selection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "fast online object tracking and segmentation a unifying approach"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "learning dynamic memory networks for object tracking"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-7 (induced by source paper "siamese cascaded region proposal networks for real time visual tracking"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"
    - "vital visual tracking via adversarial learning"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-8 (induced by source paper "atom accurate tracking by overlap maximization"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "correlation tracking via joint discrimination and reliability learning"

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)