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 ii processing and analyzing signals on graphs"):
  Abstract: The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet illustrative and general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Discrete Fourier Transform (GDFT). The spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev polynomial approximation series. Ideas related to the sampling of graph signals are presented and further linked with compressive sensing. Localized graph signal analysis in the joint vertex-spectral domain is referred to as the vertex-frequency analysis, since it can be considered as an extension of classical time-frequency analysis to the graph domain of a signal. Important topics related to the local graph Fourier transform (LGFT) are covered, together with its various forms including the graph spectral and vertex domain windows and the inversion conditions and relations. A link between the LGFT with spectral varying window and the spectral graph wavelet transform (SGWT) is also established. Realizations of the LGFT and SGWT using polynomial (Chebyshev) approximations of the spectral functions are further considered. Finally, energy versions of the vertex-frequency representations are introduced.

Includes members:
  Member "an intuitive derivation of the coherence index relation in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "graph filters and the z laplacian":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient network navigation with partial information")
  Member "stationary signal processing on graphs":
    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 "stationary graph processes and spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "hilbert transform analytic signal and modulation analysis for graph signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "kernel regression for signals over graphs")
  Member "efficient sampling set selection for bandlimited graph signals using graph spectral proxies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "edgenets edge varying graph neural networks")
  Member "subgraph based filterbanks for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "region adaptive graph fourier transform for 3d point clouds")
  Member "stationary time vertex signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stationary signal processing 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)