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: "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
  Abstract: We study the question of extracting a sequence of functions $\{\boldsymbol{f}_i, \boldsymbol{g}_i\}_{i=1}^s$ from observing only the sum of their convolutions, i.e., from $\boldsymbol{y} = \sum_{i=1}^s \boldsymbol{f}_i\ast \boldsymbol{g}_i$. While convex optimization techniques are able to solve this joint blind deconvolution-demixing problem provably and robustly under certain conditions, for medium-size or large-size problems we need computationally faster methods without sacrificing the benefits of mathematical rigor that come with convex methods. In this paper, we present a non-convex algorithm which guarantees exact recovery under conditions that are competitive with convex optimization methods, with the additional advantage of being computationally much more efficient. Our two-step algorithm converges to the global minimum linearly and is also robust in the presence of additive noise. While the derived performance bounds are suboptimal in terms of the information-theoretic limit, numerical simulations show remarkable performance even if the number of measurements is close to the number of degrees of freedom. We discuss an application of the proposed framework in wireless communications in connection with the Internet-of-Things.

Connected through hyperedge HE-1 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "over the air computation via intelligent reflecting surfaces"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "scheduling policies for federated learning in wireless networks"
    - "the roadmap to 6g ai empowered wireless networks"
    - "blind demixing for low latency communication"
    - "nonconvex demixing from bilinear measurements"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"

Connected through hyperedge HE-2 (induced by source paper "nonconvex demixing from bilinear measurements"):
  with members:
    - "blind demixing for low latency communication"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "sparse power factorization balancing peakiness and sample complexity"):
  with members:
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "solving equations of random convex functions via anchored regression"
    - "blind demixing and deconvolution at near optimal rate"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "blind demixing for low latency communication"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "painless breakups efficient demixing of low rank matrices"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "large scale convex optimization for ultra dense cloud ran"

Connected through hyperedge HE-5 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "sketching for large scale learning of mixture models"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "compressive k means"
    - "adapting to unknown noise level in sparse deconvolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"

Connected through hyperedge HE-6 (induced by source paper "secure massive iot using hierarchical fast blind deconvolution"):
  with members:
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "blind demixing and deconvolution at near optimal rate"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-7 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "fast robust and non convex subspace recovery"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"

Connected through hyperedge HE-8 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "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"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"

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)