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: "fully convolutional deep neural networks for persistent multi frame multi object detection in wide area aerial videos"
  Abstract: Multiple object detection in wide area aerial videos, has drawn the attention of the computer vision research community for a number of years. A novel framework is proposed in this paper using a fully convolutional deep neural network, which is able to detect all objects simultaneously for a given region of interest. The network is designed to accept multiple video frames at a time as the input and yields detection results for all objects in the temporally center frame. This multi-frame approach yield far better results than its single frame counterpart. Additionally, the proposed method can detect vehicles which are slowing, stopped, and/or partially or fully occluded during some frames, which cannot be handled by nearly all state-of-the-art methods. To the best of our knowledge, this is the first use of a multiple-frame, fully convolutional deep model for detecting multiple small objects and the only framework which can detect stopped and temporarily occluded vehicles, for aerial videos. The proposed network exceeds state-of-the-art results significantly on WPAFB 2009 dataset.

Connected through hyperedge HE-1 (induced by source paper "tracking in aerial hyperspectral videos using deep kernelized correlation filters"):
  with members:
    - "enkcf ensemble of kernelized correlation filters for high speed object tracking"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "learning to track at 100 fps with deep regression networks"

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)