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 unified neural network for object detection multiple object tracking and vehicle re identification"
  Abstract: Deep SORT\cite{wojke2017simple} is a tracking-by-detetion approach to multiple object tracking with a detector and a RE-ID model. #R##N#Both separately training and inference with the two model is time-comsuming. #R##N#In this paper, we unify the detector and RE-ID model into an end-to-end network, by adding an additional track branch for tracking in Faster RCNN architecture. With a unified network, we are able to train the whole model end-to-end with multi loss, which has shown much benefit in other recent works. #R##N#The RE-ID model in Deep SORT needs to use deep CNNs to extract feature map from detected object images, However, track branch in our proposed network straight make use of #R##N#the RoI feature vector in Faster RCNN baseline, which reduced the amount of calculation. #R##N#Since the single image lacks the same object which is necessary when we use the triplet loss to optimizer the track branch, we concatenate the neighbouring frames in a video to construct our training dataset. #R##N#We have trained and evaluated our model on AIC19 vehicle tracking dataset, experiment shows that our model with resnet101 backbone can achieve 57.79 \% mAP and track vehicle well.

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)