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: "sp net one shot fingerprint singular point detector"
  Abstract: Singular points of a fingerprint image are special locations having high curvature properties. They can play a pivotal role in fingerprint normalization and reliable feature extraction. Accurate and efficient extraction of a singular point plays a major role in successful fingerprint recognition and indexing. In this paper, a novel deep learning based architecture is proposed for one shot (end-to-end) singular point detection from an input fingerprint image. The model consists of a Macro-Localization Network and a Micro-Regression Network along with three stacked hourglass as a bottleneck. The proposed model has been tested on three databases viz. FVC2002 DB1_A, FVC2002 DB2_A and FPL30K and has been found to achieve true detection rate of 98.75%, 97.5% and 92.72% respectively, which is better than any other state-of-the-art technique.

Connected through hyperedge HE-1 (induced by source paper "rose real one stage effort to detect the fingerprint singular point based on multi scale spatial attention"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "cbam convolutional block attention module"
    - "towards accurate multi person pose estimation in the wild"
    - "ssd single shot multibox detector"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

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)