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: "sampling and recovery of pulse streams"
  Abstract: Compressive sensing (CS) is a new technique for the efficient acquisition of signals, images and other data that have a sparse representation in some basis, frame, or dictionary. By sparse we mean that the N-dimensional basis representation has just K <;<; N significant coefficients; in this case, the CS theory maintains that just M = O( K log N) random linear signal measurements will both preserve all of the signal information and enable robust signal reconstruction in polynomial time. In this paper, we extend the CS theory to pulse stream data, which correspond to S -sparse signals/images that are convolved with an unknown F-sparse pulse shape. Ignoring their convolutional structure, a pulse stream signal is K = SF sparse. Such signals figure prominently in a number of applications, from neuroscience to astronomy. Our specific contributions are threefold. First, we propose a pulse stream signal model and show that it is equivalent to an infinite union of subspaces. Second, we derive a lower bound on the number of measurements M required to preserve the essential information present in pulse streams. The bound is linear in the total number of degrees of freedom S + F, which is significantly smaller than the naive bound based on the total signal sparsity K = SF. Third, we develop an efficient signal recovery algorithm that infers both the shape of the impulse response as well as the locations and amplitudes of the pulses. The algorithm alternatively estimates the pulse locations and the pulse shape in a manner reminiscent of classical deconvolution algorithms. Numerical experiments on synthetic and real data demonstrate the advantages of our approach over standard CS.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "a fast algorithm for separated sparsity via perturbed lagrangians"):
  with members:
    - "super resolution off the grid"
    - "approximation algorithms for model based compressive sensing"
    - "structured sparsity through convex optimization"
    - "structured compressed sensing from theory to applications"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

Connected through hyperedge HE-4 (induced by source paper "compression based compressed sensing"):
  with members:
    - "universal compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "sharp recovery bounds for convex demixing with applications"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "compressive deconvolution in medical ultrasound imaging"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "relaxing tight frame condition in parallel proximal methods for signal restoration"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-6 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"):
  with members:
    - "identifiability scaling laws in bilinear inverse problems"
    - "on the stability of sparse convolutions"
    - "ripless compressed sensing from anisotropic measurements"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-7 (induced by source paper "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"):
  with members:
    - "sparse phase retrieval convex algorithms and limitations"
    - "blind deconvolution using convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-8 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "stable image reconstruction using total variation minimization"
    - "minimum complexity pursuit stability analysis"
    - "optimal phase transitions in compressed sensing"
    - "minimum complexity pursuit"
    - "model based compressive sensing"

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)