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: "adaptive least mean squares estimation of graph signals"
  Abstract: The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the performance of the proposed method. Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy. Finally, we apply the proposed method to build the power spatial density cartography of a given operational region in a cognitive network environment.

Connected through hyperedge HE-1 (induced by source paper "adaptive propagation graph convolutional network"):
  with members:
    - "constant time graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "diffusion improves graph learning"
    - "fast graph representation learning with pytorch geometric"
    - "a gentle introduction to deep learning for graphs"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph"):
  with members:
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-3 (induced by source paper "greedy sampling of graph signals"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"

Connected through hyperedge HE-4 (induced by source paper "adaptive graph signal processing algorithms and optimal sampling strategies"):
  with members:
    - "greedy sampling of graph signals"
    - "autoregressive moving average graph filtering"
    - "rethinking sketching as sampling a graph signal processing approach"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "local set based graph signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "improving graph convolutional networks with non parametric activation functions"):
  with members:
    - "neural graph machines learning neural networks using graphs"
    - "convolutional neural networks via node varying graph filters"
    - "geometric deep learning going beyond euclidean data"
    - "learning to represent programs with graphs"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "semi supervised learning with ladder networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (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"
    - "2 dimensional finite impulse response graph temporal filters"
    - "distributed autoregressive moving average graph 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-7 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"

Connected through hyperedge HE-8 (induced by source paper "kernel based reconstruction of space time functions on dynamic graphs"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "local set based graph signal reconstruction"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "a unifying model for representing time varying graphs"
    - "dynamic network delay cartography"

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.LG(Machine Learning)