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: "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.

Connected through hyperedge HE-1 (induced by source paper "blind demixing for low latency communication"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "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-2 (induced by source paper "heterogeneous multireference alignment a single pass approach"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "the sample complexity of multi reference alignment"
    - "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"
    - "a max cut approach to heterogeneity in cryo electron microscopy"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "fundamental limits in multi image alignment"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

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)