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: "signal recovery on incoherent manifolds"
  Abstract: Suppose that we observe noisy linear measurements of an unknown signal that can be modeled as the sum of two component signals, each of which arises from a nonlinear sub-manifold of a high dimensional ambient space. We introduce SPIN, a first order projected gradient method to recover the signal components. Despite the nonconvex nature of the recovery problem and the possibility of underdetermined measurements, SPIN provably recovers the signal components, provided that the signal manifolds are incoherent and that the measurement operator satisfies a certain restricted isometry property. SPIN significantly extends the scope of current recovery models and algorithms for low dimensional linear inverse problems and matches (or exceeds) the current state of the art in terms of performance.

Connected through hyperedge HE-1 (induced by source paper "algorithmic aspects of inverse problems using generative models"):
  with members:
    - "rate optimal denoising with deep neural networks"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"

Connected through hyperedge HE-2 (induced by source paper "low complexity implementation of convex optimization based phase retrieval"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "the achievable performance of convex demixing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-3 (induced by source paper "sparse signal processing concepts for efficient 5g system design"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "robust iterative interference alignment for cellular networks with limited feedback"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "the achievable performance of convex demixing"
    - "a stability result for sparse convolutions"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "five disruptive technology directions for 5g"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"

Connected through hyperedge HE-4 (induced by source paper "corrupted sensing novel guarantees for separating structured signals"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp recovery bounds for convex demixing with applications"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "simple bounds for recovering low complexity models"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-5 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "stable image reconstruction using total variation minimization"
    - "minimum complexity pursuit stability analysis"
    - "sampling and recovery of pulse streams"
    - "optimal phase transitions in compressed sensing"
    - "minimum complexity pursuit"
    - "model based compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "new analysis of manifold embeddings and signal recovery from compressive measurements"):
  with members:
    - "matched filtering from limited frequency samples"
    - "the restricted isometry property for random block diagonal matrices"
    - "stable manifold embeddings with structured random matrices"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "projection design for statistical compressive sensing a tight frame based approach"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "how well can we estimate a sparse vector"
    - "model based compressive sensing"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-8 (induced by source paper "minimum complexity pursuit for universal compressed sensing"):
  with members:
    - "an mcmc approach to universal lossy compression of analog sources"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "compressed sensing over ell_p balls minimax mean square error"
    - "model based compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed 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)