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: "ratm recurrent attentive tracking model"
  Abstract: We present an attention-based modular neural framework for computer vision. The framework uses a soft attention mechanism allowing models to be trained with gradient descent. It consists of three modules: a recurrent attention module controlling where to look in an image or video frame, a feature-extraction module providing a representation of what is seen, and an objective module formalizing why the model learns its attentive behavior. The attention module allows the model to focus computation on task-related information in the input. We apply the framework to several object tracking tasks and explore various design choices. We experiment with three data sets, bouncing ball, moving digits and the real-world KTH data set. The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

Connected through hyperedge HE-3 (induced by source paper "end to end recurrent multi object tracking and trajectory prediction with relational reasoning"):
  with members:
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "deep sets"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"

Connected through hyperedge HE-4 (induced by source paper "meta tracker fast and robust online adaptation for visual object trackers"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "learned optimizers that scale and generalize"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "meta sgd learning to learn quickly for few shot learning"
    - "crest convolutional residual learning for visual tracking"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "the unmanned aerial vehicle benchmark object detection and tracking"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "drone based object counting by spatially regularized regional proposal network"
    - "ron reverse connection with objectness prior networks for object detection"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-6 (induced by source paper "fpan fine grained and progressive attention localization network for data retrieval"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "dssd deconvolutional single shot detector"
    - "dual deep network for visual tracking"
    - "ensemble of part detectors for simultaneous classification and localization"
    - "stnet selective tuning of convolutional networks for object localization"
    - "progressive attention networks for visual attribute prediction"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "a twofold siamese network for real time object tracking"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "hierarchical attentive recurrent tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "squeeze and excitation networks"
    - "large margin object tracking with circulant feature maps"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"

Connected through hyperedge HE-8 (induced by source paper "good features to correlate for visual tracking"):
  with members:
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "sanet structure aware network for visual tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "modeling and propagating cnns in a tree structure 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.LG(Machine Learning)