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: "embedding graph auto encoder with joint clustering via adjacency sharing"
  Abstract: Graph convolution networks have attracted many attentions and several graph auto-encoder based clustering models are developed for attributed graph clustering. However, most existing approaches separate clustering and optimization of graph auto-encoder into two individual steps. In this paper, we propose a graph convolution network based clustering model, namely, Embedding Graph Auto-Encoder with JOint Clustering via Adjacency Sharing (\textit{EGAE-JOCAS}). As for the embedded model, we develop a novel joint clustering method, which combines relaxed k-means and spectral clustering and is applicable for the learned embedding. The proposed joint clustering shares the same adjacency within graph convolution layers. Two parts are optimized simultaneously through performing SGD and taking close-form solutions alternatively to ensure a rapid convergence. Moreover, our model is free to incorporate any mechanisms (e.g., attention) into graph auto-encoder. Extensive experiments are conducted to prove the superiority of EGAE-JOCAS. Sufficient theoretical analyses are provided to support the 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.LG(Machine Learning)