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: "a twofold siamese network for real time object tracking"
  Abstract: Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks.

Connected through hyperedge HE-1 (induced by source paper "spark spatial aware online incremental attack against visual tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "a hybrid approach for tracking individual players in broadcast match videos"):
  with members:
    - "soccernet a scalable dataset for action spotting in soccer videos"
    - "end to end representation learning for correlation filter based tracking"
    - "fully convolutional siamese networks for object tracking"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-3 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "got 10k a large high diversity benchmark for generic object tracking in the wild"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "class balanced loss based on effective number of samples"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"

Connected through hyperedge HE-5 (induced by source paper "visual tracking via dynamic memory networks"):
  with members:
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "mavot memory augmented video object tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-6 (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"
    - "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"
    - "towards a better match in siamese network based visual object tracker"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-7 (induced by source paper "real time correlation tracking via joint model compression and transfer"):
  with members:
    - "unsupervised deep tracking"
    - "real time mdnet"
    - "distractor aware siamese networks for visual object tracking"
    - "good features to correlate for visual tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "deep attentive tracking via reciprocative learning"
    - "unveiling the power of deep tracking"
    - "correlation tracking via joint discrimination and reliability learning"

Connected through hyperedge HE-8 (induced by source paper "leveraging shape completion for 3d siamese tracking"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "learning depth with convolutional spatial propagation network"
    - "real time mdnet"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "segstereo exploiting semantic information for disparity estimation"
    - "distractor aware siamese networks for visual object tracking"
    - "learning 3d shape completion under weak supervision"

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)