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: "spatio colour asplund s metric and logarithmic image processing for colour images lipc"
  Abstract: Asplund’s metric, which is useful for pattern matching, consists in a double-sided probing, i.e. the over-graph and the sub-graph of a function are probed jointly. This paper extends the Asplund’s metric we previously defined for colour and multivariate images using a marginal approach (i.e. component by component) to the first spatio-colour Asplund’s metric based on the vectorial colour LIP model (LIPC). LIPC is a non-linear model with operations between colour images which are consistent with the human visual system. The defined colour metric is insensitive to lighting variations and a variant which is robust to noise is used for colour pattern matching.

Connected through hyperedge HE-1 (induced by source paper "region homogeneity in the logarithmic image processing framework application to region growing algorithms"):
  with members:
    - "homogeneity of a region in the logarithmic image processing framework application to region growing algorithms"
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "superimposition of eye fundus images for longitudinal analysis from large public health databases"
    - "improving shadow suppression for illumination robust face recognition"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-2 (induced by source paper "functional asplund s metrics for pattern matching robust to variable lighting conditions"):
  with members:
    - "logarithmic mathematical morphology a new framework adaptive to illumination changes"
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "superimposition of eye fundus images for longitudinal analysis from large public health databases"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-3 (induced by source paper "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"):
  with members:
    - "aspl u nd s metric defined in the logarithmic image processing lip framework for colour and multivariate images"

Connected through hyperedge HE-4 (induced by source paper "speeding up the k o hler s method of contrast thresholding"):
  with members:
    - "yfcc100m the new data in multimedia research"

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)