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: "compressed subspace matching on the continuum"
  Abstract: We consider the general problem of matching a subspace to a signal in R^N that has been observed indirectly (compressed) through a random projection. We are interested in the case where the collection of K-dimensional subspaces is continuously parameterized, i.e. naturally indexed by an interval from the real line, or more generally a region of R^D. Our main results show that if the dimension of the random projection is on the order of K times a geometrical constant that describes the complexity of the collection, then the match obtained from the compressed observation is nearly as good as one obtained from a full observation of the signal. We give multiple concrete examples of collections of subspaces for which this geometrical constant can be estimated, and discuss the relevance of the results to the general problems of template matching and source localization.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"

Connected through hyperedge HE-2 (induced by source paper "compressive sampling of ensembles of correlated signals"):
  with members:
    - "compressive multiplexing of correlated signals"
    - "blind deconvolution using convex programming"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "recovering low rank matrices from few coefficients in any basis"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-3 (induced by source paper "recipes for stable linear embeddings from hilbert spaces to ℝ m"):
  with members:
    - "sketching for simultaneously sparse and low rank covariance matrices"
    - "isometric sketching of any set via the restricted isometry property"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "low rank matrix recovery from rank one measurements"
    - "uniform recovery of fusion frame structured sparse signals"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "identifiability and stability in blind deconvolution under minimal assumptions"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and biconvex compressive sensing"
    - "information theoretic limits of matrix completion"
    - "blind recovery of sparse signals from subsampled convolution"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (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"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "compressive matched field processing"
    - "compressed sensing performance bounds under poisson noise"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "compressive multiplexing of correlated signals"):
  with members:
    - "sparse recovery of streaming signals using l1 homotopy"
    - "blind deconvolution using convex programming"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "towards a mathematical theory of super resolution"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "recovering low rank matrices from few coefficients in any basis"
    - "xampling signal acquisition and processing in union of subspaces"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"

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)