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: "subspace pursuit for compressive sensing signal reconstruction"
  Abstract: We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of LP optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean squared error of the reconstruction is upper bounded by constant multiples of the measurement and signal perturbation energies.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "lazier than lazy greedy"
    - "greedy column subset selection for large scale data sets"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-3 (induced by source paper "random access for massive machine type communications"):
  with members:
    - "beamwidth control for noma in hybrid mmwave communication systems"
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "on the performance gain of noma over oma in uplink single cell systems"
    - "multi beam noma for hybrid mmwave systems"
    - "joint pilot and payload power control for uplink mimo noma with mrc sic receivers"
    - "reliability based windowed decoding for spatially coupled ldpc codes"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "a multi beam noma framework for hybrid mmwave systems"

Connected through hyperedge HE-4 (induced by source paper "tree search network for sparse regression"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "adaptive feature selection computationally efficient online sparse linear regression under rip"
    - "admm net a deep learning approach for compressive sensing mri"
    - "distributed compressive sensing a deep learning approach"

Connected through hyperedge HE-5 (induced by source paper "joint sparse recovery using signal space matching pursuit"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"
    - "recovery of sparse signals via generalized orthogonal matching pursuit a new analysis"
    - "generalized orthogonal matching pursuit"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"

Connected through hyperedge HE-6 (induced by source paper "sparse signal recovery via generalized entropy functions minimization"):
  with members:
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "sparsity averaging for compressive imaging"
    - "oblique pursuits for compressed sensing"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"
    - "robust principal component analysis"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "stochastic greedy closing the optimality gap in exact weak submodular maximization"):
  with members:
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "lazier than lazy greedy"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "active semi supervised learning using submodular functions"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-8 (induced by source paper "a novel efficient approach with data adaptive capability for omp based sparse subspace clustering"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "active dictionary learning in sparse representation based classification"
    - "greedy feature selection for subspace clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "a tutorial on spectral clustering"

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.NA(Numerical Analysis)