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: "graph learning for spatiotemporal signal with long short term characterization"
  Abstract: Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology and financial analysis, etcetera. Due to the widespread correlation in diverse applications, ideas that taking full advantage of correlated property to find meaningful insights of spatiotemporal signals have begun to emerge. In this paper, we study the problem of uncovering graphs that better reveal the relations behind data, with the help of long and short term correlated property in spatiotemporal signals. A spatiotemporal signal model considering both spatial and temporal relationship is firstly presented. Particularly, a low-rank representation together with a Gaussian Markov process is adopted to describe the signals' time-correlated behavior. Next, we cast the graph learning problem as a joint low-rank component estimation and graph Laplacian inference problem. A Low-Rank and Spatiotemporal Smoothness-based graph learning method (GL-LRSS) is proposed, which novelly introduces spatiotemporal smooth prior to the field of time-vertex signal analysis. Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved. Experiments on both synthetic and real-world datasets demonstrate the significant improvement on learning accuracy of the proposed GL-LRSS over current state-of-the-art low-rank estimation and graph learning methods.

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.SI(Social and Information Networks)