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: "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"
  Abstract: This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images. This method relies on benefiting unsupervised learning, in the form of image reconstruction, for the network training. To this end, it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single task and proposes to learn this united task by a single generative model. This embedding generates a multi-channel output image by superimposing an original input image on its segmentation map. Then, the method learns to translate the input image to this embedded output image using a conditional generative adversarial network, which is known to be quite effective for image-to-image translations. This proposal is different than the existing approach that uses image reconstruction for the same regularization purpose. The existing approach considers segmentation and image reconstruction as two separate tasks in a multi-task network, defines their losses independently, and then combines these losses in a joint loss function. However, the definition of such a function requires externally determining the right contribution amounts of the supervised and unsupervised losses that yield balanced learning between the segmentation and image reconstruction tasks. The proposed approach eliminates this difficulty by uniting these two tasks into a single one, which intrinsically combines their losses. Using histopathological image segmentation as a showcase application, our experiments demonstrate that this proposed approach leads to better segmentation results.

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)