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 "distributed linear network operators using graph filters"):
  Abstract: We study the design of graph filters to implement arbitrary linear transformations between graph signals. Graph filters can be represented by matrix polynomials of the graph-shift operator, which captures the structure of the graph and is assumed to be given. Thus, graph-filter design consists in choosing the coefficients of these polynomials (known as filter coefficients) to resemble desired linear transformations. Due to the local structure of the graph-shift operator, graph filters can be implemented distributedly across nodes, making them suitable for networked settings. We determine spectral conditions under which a specific linear transformation can be implemented perfectly using graph filters. Furthermore, for the cases where perfect implementation is infeasible, the design of optimal approximations for different error metrics is analyzed. We introduce the notion of a node-variant graph filter, which allows the simultaneous implementation of multiple (regular) graph filters in different nodes of the graph. This additional flexibility enables the design of more general operators without undermining the locality in implementation. Perfect and approximate implementation of network operators is also studied for node-variant graph filters. We demonstrate the practical relevance of the developed framework by studying in detail the application of graph filters to the problems of finite-time consensus and analog network coding. Finally, we present additional numerical experiments comparing the performance of node-invariant and node-variant filters when approximating arbitrary linear network operators.

Includes members:
  Member "stationary graph processes and spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "reconstruction of graph signals through percolation from seeding nodes":
    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 "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "sampling of graph signals with successive local aggregations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "discrete signal processing on graphs sampling theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "discrete signal processing on graphs frequency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edgenets edge varying graph neural networks")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)