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: "distributed autoregressive moving average graph filters"
  Abstract: We introduce the concept of autoregressive moving average (ARMA) filters on a graph and show how they can be implemented in a distributed fashion. Our graph filter design philosophy is independent of the particular graph, meaning that the filter coefficients are derived irrespective of the graph. In contrast to finite-impulse response (FIR) graph filters, ARMA graph filters are robust against changes in the signal and/or graph. In addition, when time-varying signals are considered, we prove that the proposed graph filters behave as ARMA filters in the graph domain and, depending on the implementation, as first or higher order ARMA filters in the time domain.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "graph neural networks with convolutional arma filters"
    - "a fair comparison of graph neural networks for graph classification"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "simplifying graph convolutional networks"
    - "learning individual treatment effects from networked observational data"

Connected through hyperedge HE-3 (induced by source paper "graph neural networks with convolutional arma filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "on the transferability of spectral graph filters"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "stationary graph processes and spectral estimation"):
  with members:
    - "blind identification of graph filters"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "subsampling for graph power spectrum estimation"
    - "distributed linear network operators using graph filters"

Connected through hyperedge HE-5 (induced by source paper "filtering random graph processes over random time varying graphs"):
  with members:
    - "autoregressive moving average graph filtering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "2 dimensional finite impulse response graph temporal filters"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "autoregressive moving average graph filtering"):
  with members:
    - "frequency analysis of temporal graph signals"
    - "learning laplacian matrix in smooth graph signal representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "chebyshev polynomial approximation for distributed signal processing"

Connected through hyperedge HE-7 (induced by source paper "scalable m channel critically sampled filter banks for graph signals"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "structured sampling and fast reconstruction of smooth graph signals"
    - "fast eigenspace approximation using random signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "subgraph based filterbanks for graph signals"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-8 (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"
    - "towards stationary time vertex signal processing"
    - "subsampling for graph power spectrum estimation"

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.SI(Social and Information Networks)