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: "fingertip detection a fast method with natural hand"
  Abstract: Many vision based applications have used fingertips to track or manipulate gestures in their applications. Gesture identification is a natural way to pass the signals to the machine, as the human express its feelings most of the time with hand expressions. Here a novel time efficient algorithm has been described for fingertip detection. This method is invariant to hand direction and in preprocessing it cuts only hand part from the full image, hence further computation would be much faster than processing full image. Binary silhouette of the input image is generated using HSV color space based skin filter and hand cropping done based on intensity histogram of the hand image.

Connected through hyperedge HE-1 (induced by source paper "finger stylus for non touch enable systems"):
  with members:
    - "intelligent approaches to interact with machines using hand gesture recognition in natural way a survey"
    - "automated switching system for skin pixel segmentation in varied lighting"
    - "a health monitoring system for elder and sick persons"
    - "an efficient real time method of fingertip detection"

Connected through hyperedge HE-2 (induced by source paper "fingers angle calculation using level set method"):
  with members:
    - "tracking of fingertips and centres of palm using kinect"
    - "automated switching system for skin pixel segmentation in varied lighting"

Connected through hyperedge HE-3 (induced by source paper "automated switching system for skin pixel segmentation in varied lighting"):
  with members:
    - "abhivyakti a vision based intelligent system for elder and sick persons"
    - "comparative study of statistical skin detection algorithms for sub continental human images"

Connected through hyperedge HE-4 (induced by source paper "real time fabric defect detection system on an embedded dsp platform"):
  with members:
    - "product line development architectural model"

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)