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: "using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels"
  Abstract: The recognition of materials and objects inside transparent containers using computer vision has a wide range of applications, ranging from industrial bottles filling to the automation of chemistry laboratory. One of the main challenges in such recognition is the ability to distinguish between image features resulting from the vessels surface and image features resulting from the material inside the vessel. Reflections and the functional parts of a vessels surface can create strong edges that can be mistakenly identified as corresponding to the vessel contents, and cause recognition errors. The ability to evaluate whether a specific edge in an image stems from the vessels surface or from its contents can considerably improve the ability to identify materials inside transparent vessels. This work will suggest a method for such evaluation, based on the following two assumptions: 1) Areas of high curvature on the vessel surface are likely to cause strong edges due to changes in reflectivity, as is the appearance of functional parts (e.g. corks or valves). 2) Most transparent vessels (bottles, glasses) have high symmetry (cylindrical). As a result the curvature angle of the vessels surface at each point of the image is similar to the curvature angle of the contour line of the vessel in the same row in the image. These assumptions, allow the identification of image regions with strong edges corresponding to the vessel surface reflections. Combining this method with existing image analysis methods for detecting materials inside transparent containers allows considerable improvement in accuracy.

Connected through hyperedge HE-1 (induced by source paper "manifold denoising by nonlinear robust principal component analysis"):
  with members:
    - "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"
    - "robust pca with partial subspace knowledge"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"):
  with members:
    - "tracing the boundaries of materials in transparent vessels using computer vision"
    - "computer vision based recognition of liquid surfaces and phase boundaries in transparent vessels with emphasis on chemistry applications"

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)