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: "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
  Abstract: The success of the compressed sensing paradigm has shown that a substantial reduction in sampling and storage complexity can be achieved in certain linear and non-adaptive estimation problems. It is therefore an advisable strategy for noncoherent information retrieval in, for example, sporadic blind and semi-blind communication and sampling problems. But, the conventional model is not practical here since the compressible signals have to be estimated from samples taken solely on the output of an un-calibrated system which is unknown during measurement but often compressible. Conventionally, one has either to operate at suboptimal sampling rates or the recovery performance substantially suffers from the dominance of model mismatch. In this work we discuss such type of estimation problems and we focus on bilinear inverse problems. We link this problem to the recovery of low-rank and sparse matrices and establish stable low-dimensional embeddings of the uncalibrated receive signals whereby addressing also efficient communication-oriented methods like universal random demodulation. Exemplarily, we investigate in more detail sparse convolutions serving as a basic communication channel model. In using some recent results from additive combinatorics we show that such type of signals can be efficiently low-rate sampled by semi-blind methods. Finally, we present a further application of these results in the field of phase retrieval from intensity Fourier measurements.

Connected through hyperedge HE-1 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"

Connected through hyperedge HE-2 (induced by source paper "noncoherent short packet communication via modulation on conjugated zeros"):
  with members:
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "short message communication and fir system identification using huffman sequences"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "sparse signal processing concepts for efficient 5g system design"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-3 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovering structured data from superimposed non linear measurements"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "low rank tensor recovery via iterative hard thresholding"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "recovering structured data from superimposed non linear measurements"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "the generalized lasso for sub gaussian measurements with dithered quantization"
    - "self calibration and biconvex compressive sensing"
    - "the generalized lasso with non linear observations"
    - "blind recovery of sparse signals from subsampled convolution"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-5 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "blind demixing and deconvolution at near optimal rate"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "explicit frames for deterministic phase retrieval via phaselift"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "on the stability of sparse convolutions"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-6 (induced by source paper "empirical chaos processes and blind deconvolution"):
  with members:
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "rip like properties in subsampled blind deconvolution"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "identifiability and stability in blind deconvolution under minimal assumptions"

Connected through hyperedge HE-7 (induced by source paper "compressive rate estimation with applications to device to device communications"):
  with members:
    - "robust iterative interference alignment for cellular networks with limited feedback"
    - "spectrum sharing scheme between cellular users and ad hoc device to device users"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "benchmarking practical rrm algorithms for d2d communications in lte advanced"
    - "power control for d2d underlaid cellular networks modeling algorithms and analysis"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "robust analog function computation via wireless multiple access channels"

Connected through hyperedge HE-8 (induced by source paper "on the stability of sparse convolutions"):
  with members:
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "the restricted isometry property for time frequency structured random matrices"
    - "restricted isometries for partial random circulant matrices"

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)