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: "blind identification of graph filters"
  Abstract: Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of  linear graph filters  modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal   ${\mathbf y}$   modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients    ${\mathbf h}$  , and the input signal    ${\mathbf x}$   which is assumed to be sparse. While   ${\mathbf y}$   is a bilinear function of   ${\mathbf x}$   and    ${\mathbf h}$  , the filtered graph signal is also a linear combination of the entries of the lifted rank-one, row-sparse matrix    ${\mathbf x}{\mathbf h}^T$  . The blind graph-filter identification problem can thus be tackled via rank and sparsity minimization subject to linear constraints, an inverse problem amenable to convex relaxations offering provable recovery guarantees under simplifying assumptions. Numerical tests using both synthetic and real-world networks illustrate the merits of the proposed algorithms, as well as the benefits of leveraging multiple signals to aid the blind identification task.

Connected through hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs"):
  with members:
    - "network inference from consensus dynamics with unknown parameters"
    - "blind inference of centrality rankings from graph signals"
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "active sensing of social networks"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"

Connected through hyperedge HE-3 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "spectral projector based graph fourier transforms"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "fast resampling of three dimensional point clouds via graphs"):
  with members:
    - "greedy sampling of graph signals"
    - "convergence analysis of the information matrix in gaussian belief propagation"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "blind community detection from low rank excitations of a graph filter"):
  with members:
    - "latent agents in networks estimation and pricing"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "autoregressive moving average graph filtering"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "learning sparse graphs under smoothness prior"
    - "compressive spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "median activation functions for graph neural networks"):
  with members:
    - "improving graph convolutional networks with non parametric activation functions"
    - "graph convolutional neural networks for web scale recommender systems"
    - "snap a general purpose network analysis and graph mining library"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "authorship attribution through function word adjacency networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "blind identification of invertible graph filters with multiple sparse inputs"):
  with members:
    - "network infusion to infer information sources in networks"
    - "source localization on graphs via l1 recovery and spectral graph theory"
    - "self calibration and biconvex compressive sensing"
    - "discrete signal processing on graphs"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "blind deconvolution using convex programming"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-8 (induced by source paper "spectral projector based graph fourier transforms"):
  with members:
    - "graph equivalence classes for spectral projector based graph fourier transforms"
    - "autoregressive moving average graph filtering"
    - "signal processing on graphs causal modeling of un structured data"
    - "agile inexact methods for spectral projector based graph fourier transforms"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs variation minimization"
    - "learning parametric dictionaries for signals on graphs"

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.IT(Information Theory)