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 "graph signal processing part iii machine learning on graphs from graph topology to applications"):
  Abstract: Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution. Part III of this monograph starts by addressing ways to learn graph topology, from the case where the physics of the problem already suggest a possible topology, through to most general cases where the graph topology is learned from the data. A particular emphasis is on graph topology definition based on the correlation and precision matrices of the observed data, combined with additional prior knowledge and structural conditions, such as the smoothness or sparsity of graph connections. For learning sparse graphs (with small number of edges), the least absolute shrinkage and selection operator, known as LASSO is employed, along with its graph specific variant, graphical LASSO. For completeness, both variants of LASSO are derived in an intuitive way, and explained. An in-depth elaboration of the graph topology learning paradigm is provided through several examples on physically well defined graphs, such as electric circuits, linear heat transfer, social and computer networks, and spring-mass systems. As many graph neural networks (GNN) and convolutional graph networks (GCN) are emerging, we have also reviewed the main trends in GNNs and GCNs, from the perspective of graph signal filtering. Tensor representation of lattice-structured graphs is next considered, and it is shown that tensors (multidimensional data arrays) are a special class of graph signals, whereby the graph vertices reside on a high-dimensional regular lattice structure. This part of monograph concludes with two emerging applications in financial data processing and underground transportation networks modeling.

Includes members:
  Member "learning graphs from data a signal representation perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "graphsac detecting anomalies in large scale graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "power systems topology and state estimation by graph blind source separation":
    also connected through other hyperedges:
      (none)
  Member "semi blind inference of topologies and dynamical processes over dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks")
  Member "graph signal processing part i graphs graph spectra and spectral clustering":
    also connected through other hyperedges:
      (none)
  Member "star gcn stacked and reconstructed graph convolutional networks for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "magnn metapath aggregated graph neural network for heterogeneous graph embedding")
  Member "graph signal processing part ii processing and analyzing signals on graphs":
    also connected through other hyperedges:
      (none)
  Member "an intuitive derivation of the coherence index relation in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")

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.IT(Information Theory)