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: "towards stationary time vertex signal processing"
  Abstract: Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods for signal processing on graphs ignore the dimension of time, treating successive graph signals independently or taking a global average. To address this shortcoming, this paper considers the statistical analysis of time-varying graph signals. We introduce a novel definition of joint (time-vertex) stationarity, which generalizes the classical definition of time stationarity and the more recent definition appropriate for graphs. Joint stationarity gives rise to a scalable Wiener optimization framework for joint denoising, semi-supervised learning, or more generally inversing a linear operator, that is provably optimal. Experimental results on real weather data demonstrate that taking into account graph and time dimensions jointly can yield significant accuracy improvements in the reconstruction effort.

Connected through hyperedge HE-1 (induced by source paper "stationary signal processing on graphs"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "fast robust pca on graphs"
    - "stationary time vertex signal processing"
    - "subsampling for graph power spectrum estimation"
    - "a probabilistic interpretation of sampling theory of graph signals"
    - "gspbox a toolbox for signal processing on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"

Connected through hyperedge HE-2 (induced by source paper "a time vertex signal processing framework"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "fast robust pca on graphs"
    - "frequency analysis of temporal graph signals"
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "pde approaches to graph analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-3 (induced by source paper "stationary time vertex signal processing"):
  with members:
    - "inference of spatio temporal functions over graphs via multikernel kriged kalman filtering"
    - "autoregressive moving average graph filtering"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "frequency analysis of temporal graph signals"
    - "subsampling for graph power spectrum estimation"
    - "a probabilistic interpretation of sampling theory of graph signals"

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)