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: "improving precision and recall of face recognition in sipp with combination of modified mean search and lsh"
  Abstract: Although face recognition has been improved much as the development of Deep Neural Networks, SIPP(Single Image Per Person) problem in face recognition has not been better solved, especially in practical applications where searching over complicated database. In this paper, a combination of modified mean search and LSH method would be introduced orderly to improve the precision and recall of SIPP face recognition without retrain of the DNN model. First, a modified SVD based augmentation method would be introduced to get more intra-class variations even for person with only one image. Second, an unique rule based combination of modified mean search and LSH method was proposed the first time to help get the most similar personID in a complicated dataset, and some theoretical explaining followed. Third, we would like to emphasize, no need to retrain of the DNN model and would easy to be extended without much efforts. We do some practical testing in competition of Msceleb challenge-2 2017 which was hold by Microsoft Research, great improvement of coverage from 13.39% to 19.25%, 29.94%, 42.11%, 47.52% at precision 99%(P99) would be shown latter, coverage reach 94.2% and 100% at precision 97%(P97) and 95%(P95) respectively. As far as we known, this is the only paper who do not fine-tuning on competition dataset and ranked top-10. A similar test on CASIA WebFace dataset also demonstrated the same improvements on both precision and recall.

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)