human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "characterization and inference of graph diffusion processes from observations of stationary signals"):
  Abstract: Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures.

Includes members:
  Member "graph learning under sparsity priors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning graphs from data a signal representation perspective")
  Member "stationary signal processing on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "stationary graph processes and spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "network scale traffic modeling and forecasting with graphical lasso and neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "short term road traffic prediction based on deep cluster at large scale networks")
  Member "graph reconstruction from the observation of diffused signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistent tomography under partial observations over adaptive networks")
  Member "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "learning heat diffusion graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "network topology identification from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stationary graph processes and spectral estimation")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)