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 the boundaries of materials in transparent vessels using computer vision"
  Abstract: Visual recognition of material boundaries in transparent vessels is valuable for numerous applications. Such recognition is essential for estimation of fill-level, volume and phase-boundaries as well as for tracking of such chemical processes as precipitation, crystallization, condensation, evaporation and phase-separation. The problem of material boundary recognition in images is particularly complex for materials with non-flat surfaces, i.e., solids, powders and viscous fluids, in which the material interfaces have unpredictable shapes. This work demonstrates a general method for finding the boundaries of materials inside transparent containers in images. The method uses an image of the transparent vessel containing the material and the boundary of the vessel in this image. The recognition is based on the assumption that the material boundary appears in the image in the form of a curve (with various constraints) whose endpoints are both positioned on the vessel contour. The probability that a curve matches the material boundary in the image is evaluated using a cost function based on some image properties along this curve. Several image properties were examined as indicators for the material boundary. The optimal boundary curve was found using Dijkstra's algorithm. The method was successfully examined for recognition of various types of phase-boundaries, including liquid-air, solid-air and solid-liquid interfaces, as well as for various types of glassware containers from everyday life and the chemistry laboratory (i.e., bottles, beakers, flasks, jars, columns, vials and separation-funnels). In addition, the method can be easily extended to materials carried on top of carrier vessels (i.e., plates, spoons, spatulas).

Connected through hyperedge HE-1 (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-2 (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"
    - "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"
    - "coco stuff thing and stuff classes in context"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"):
  with members:
    - "using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels"
    - "computer vision based recognition of liquid surfaces and phase boundaries in transparent vessels with emphasis on chemistry applications"

Connected through hyperedge HE-4 (induced by source paper "using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels"):
  with members:
    - "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)