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 video based method for objectively rating ataxia"
  Abstract: For many movement disorders, such as Parkinson's and ataxia, disease progression is usually assessed visually by a clinician according to a numerical rating scale, or using questionnaires. These tests are subjective, time-consuming, and must be administered by a professional. We present an automated method for quantifying the severity of motion impairment in patients with ataxia, using only video recordings. We focus on videos of the finger-to-nose test, a common movement task used to assess ataxia progression during the course of routine clinical checkups. #R##N#Our method uses pose estimation and optical flow techniques to track the motion of the patient's hand in a video recording. We extract features that describe qualities of the motion such as speed and variation in performance. Using labels provided by an expert clinician, we build a supervised learning model that predicts severity according to the Brief Ataxia Rating Scale (BARS). Our model achieves a mean absolute error of 0.363 on a 0-4 scale and a prediction-label correlation of 0.835 in a leave-one-patient-out experiment. The accuracy of our system is comparable to the reported inter-rater correlation among clinicians assessing the finger-to-nose exam using a similar ataxia rating scale. This work demonstrates the feasibility of using videos to produce more objective and clinically useful measures of motor impairment.

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)