human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "painless breakups efficient demixing of low rank matrices"):
  Abstract: Assume we are given a sum of linear measurements of $s$ different rank-$r$ matrices of the form $y = \sum_{k=1}^{s} \mathcal{A}_k ({X}_k)$. When and under which conditions is it possible to extract (demix) the individual matrices ${X}_k$ from the single measurement vector ${y}$? And can we do the demixing numerically efficiently? We present two computationally efficient algorithms based on hard thresholding to solve this low rank demixing problem. We prove that under suitable conditions these algorithms are guaranteed to converge to the correct solution at a linear rate. We discuss applications in connection with quantum tomography and the Internet-of-Things. Numerical simulations demonstrate empirically the performance of the proposed algorithms.

Includes members:
  Member "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow")
  Member "convolutional dictionary learning through tensor factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "working locally thinking globally theoretical guarantees for convolutional sparse coding")
  Member "blind deconvolution meets blind demixing algorithms and performance bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "sparse signal processing concepts for efficient 5g system design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "low rank modeling and its applications in image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive survey for low rank regularization")
  Member "convexity in source separation models geometry and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning")
  Member "the achievable performance of convex demixing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain")
  Member "blind deconvolution using convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

Question: Please tell me which class the source paper 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)