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: "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"
  Abstract: Detection of boundaries of materials stored in transparent vessels is essential for identifying properties such as liquid level and phase boundaries, which are vital for controlling numerous processes in the industry and chemistry laboratory. This work presents a computer vision method for identifying the boundary of materials in transparent vessels using the graph-cut algorithm. The method receives an image of a transparent vessel containing a material and the contour of the vessel in the image. The boundary of the material in the vessel is found by the graph cut method. In general the method uses the vessel region of the image to create a graph, where pixels are vertices, and the cost of an edge between two pixels is inversely correlated with their intensity difference. The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source. The top 10% of the pixels in the vessels are assumed to correspond to the air phase and defined as the graph sink. The minimal cut that splits the resulting graph between the source and sink (hence, material and air) is traced using the max-flow/min-cut approach. This cut corresponds to the boundary of the material in the image. The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels.

Connected through hyperedge HE-1 (induced by source paper "manifold denoising by nonlinear robust principal component analysis"):
  with members:
    - "using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels"
    - "robust pca with partial subspace knowledge"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "hierarchical semantic segmentation using modular convolutional neural networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "setting an attention region for convolutional neural networks using region selective features for recognition of materials within glass vessels"
    - "objects as context for detecting their semantic parts"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic understanding of scenes through the ade20k dataset"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "setting an attention region for convolutional neural networks using region selective features for recognition of materials within glass vessels"):
  with members:
    - "semantic understanding of scenes through the ade20k dataset"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "coco stuff thing and stuff classes in context"
    - "tracing the boundaries of materials in transparent vessels using computer vision"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "abc cnn an attention based convolutional neural network for visual question answering"

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)