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: "spatially supervised recurrent convolutional neural networks for visual object tracking"
  Abstract: In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.

Connected through hyperedge HE-1 (induced by source paper "deformable object tracking with gated fusion"):
  with members:
    - "deep attentive tracking via reciprocative learning"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deformable convolutional networks"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-2 (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-3 (induced by source paper "revamp2t real time edge video analytics for multi camera privacy aware pedestrian tracking"):
  with members:
    - "omni scale feature learning for person re identification"
    - "learning incremental triplet margin for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "person re identification with deep similarity guided graph neural network"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-4 (induced by source paper "continuity stability and integration novel tracking based perspectives for temporal object detection"):
  with members:
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "detect or track towards cost effective video object detection tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "tell me what to track"):
  with members:
    - "lasot a high quality benchmark for large scale single object tracking"
    - "vital visual tracking via adversarial learning"
    - "person search with natural language description"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "where to play retrieval of video segments using natural language queries"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "detect to track and track to detect"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "an efficient and layout independent automatic license plate recognition system based on the yolo detector"):
  with members:
    - "convolutional neural networks for automatic meter reading"
    - "yolov3 an incremental improvement"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "focal loss for dense object detection"
    - "cnn for license plate motion deblurring"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "deep affinity network for multiple object tracking"):
  with members:
    - "learning non uniform hypergraph for multi object tracking"
    - "deep cosine metric learning for person re identification"
    - "benchmark data and method for real time people counting in cluttered scenes using depth sensors"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "learning regression and verification networks for long term visual tracking"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "fusion of head and full body detectors for multi object tracking"
    - "detect to track and track to detect"

Connected through hyperedge HE-8 (induced by source paper "deep object tracking on dynamic occupancy grid maps using rnns"):
  with members:
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "mask r cnn"
    - "fully convolutional neural networks for dynamic object detection in grid maps"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "ssd single shot multibox detector"
    - "u net convolutional networks for biomedical image segmentation"

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)