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: "compressive matched field processing"
  Abstract: Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by “compressing” these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green’s function can be constructed by backpropagating a small set of random receiver vectors. Then the source can be located by performing a number of “short” correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented that demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditiona...

Connected through hyperedge HE-1 (induced by source paper "grid free compressive beamforming"):
  with members:
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-2 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressive parameter estimation in awgn"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "stable manifold embeddings with structured random matrices"

Connected through hyperedge HE-3 (induced by source paper "dimensionality reduction with subgaussian matrices a unified theory"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "compressed subspace matching on the continuum"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "compressed sensing performance bounds under poisson noise"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "faster least squares approximation"

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)