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: "eco efficient convolution operators for tracking"
  Abstract: In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. #R##N#We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four benchmarks: VOT2016, UAV123, OTB-2015, and TempleColor. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain in Expected Average Overlap compared to the top ranked method in the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

Connected through hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "3d appearance super resolution with deep learning"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "gasl guided attention for sparsity learning in deep neural networks"
    - "importance estimation for neural network pruning"
    - "learning filter basis for convolutional neural network compression"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-3 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "reppoints point set representation for object detection"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-4 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation"):
  with members:
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "deep visual attention prediction"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"

Connected through hyperedge HE-5 (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"
    - "a twofold siamese network for real time object tracking"

Connected through hyperedge HE-6 (induced by source paper "multi drone based single object tracking with agent sharing network"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-7 (induced by source paper "keyfilter aware real time uav object tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "unsupervised deep tracking"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-8 (induced by source paper "end to end learning of object motion estimation from retinal events for event based object tracking"):
  with members:
    - "event based vision a survey"
    - "atom accurate tracking by overlap maximization"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "real time clustering and multi target tracking using event based sensors"

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)