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: "computing the spatial probability of inclusion inside partial contours for computer vision applications"
  Abstract: In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other region-based approaches use probabilistic analysis such as clustering and Markov random fields, but those methods cannot be used to analyze edges and their interaction. In fact, only image segmentation can produce regions based on edges, but it requires thresholding by simply separating the regions into binary in-out information. Hence, there is currently a gap between edge-based and region-based algorithms, since edges cannot be used to study the properties of a region and vice versa. The objective of this paper is to present a novel spatial probability analysis that allows determining the probability of inclusion inside a set of partial contours (strokes). To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods. The developed method is consistent with the fundamental properties of inclusion probabilities and its results are validated by comparing an image with the probability-based estimation given by our algorithm. The method can also be generalized to take into consideration the intensity of the edges or to be used for 3D shapes. This is the first documented method that allows computing a space of probability based on interacting edges, which opens the path to broader applications such as image segmentation and contour completion.

Connected through hyperedge HE-1 (induced by source paper "deep green function convolution for improving saliency in convolutional neural networks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "richer convolutional features for edge detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-2 (induced by source paper "saliency enhancement using gradient domain edges merging"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-3 (induced by source paper "improving convolutional neural networks via conservative field regularisation and integration"):
  with members:
    - "deep green function convolution for improving saliency in convolutional neural networks"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "fast and optimal laplacian solver for gradient domain image editing using green function convolution"):
  with members:
    - "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
    - "richer convolutional features for edge detection"
    - "fast edge detection using structured forests"

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)