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: "fixed rank matrix factorizations and riemannian low rank optimization"
  Abstract: Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss relative usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with the state-of-the-art and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix.

Connected through hyperedge HE-1 (induced by source paper "fast and robust spectrally sparse signal recovery a provable non convex approach via robust low rank hankel matrix reconstruction"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "accelerated alternating projections for robust principal component analysis"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-2 (induced by source paper "learning multilingual word embeddings in latent metric space a geometric approach"):
  with members:
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "a convex feature learning formulation for latent task structure discovery"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-5 (induced by source paper "riemannian optimization for skip gram negative sampling"):
  with members:
    - "towards a better understanding of predict and count models"
    - "how to generate a good word embedding"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "localization in the internet of things network a low rank matrix completion approach"):
  with members:
    - "orthogonal rank one matrix pursuit for low rank matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "topological interference management with user admission control via riemannian optimization"):
  with members:
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "wireless communications in the era of big data"
    - "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis"

Connected through hyperedge HE-8 (induced by source paper "low rank matrix completion for topological interference management by riemannian pursuit"):
  with members:
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "when does an ensemble of matrices with randomly scaled rows lose rank"
    - "feasibility of interference alignment for the mimo interference channel"
    - "a matrix completion approach to linear index coding problem"
    - "topological interference management with transmitter cooperation"
    - "topological interference management through index coding"

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.LG(Machine Learning)