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: "guaranteed rank minimization via singular value projection"
  Abstract: Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization with affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraints that satisfy the "restricted isometry property" and show robustness of our method to noise. Our results improve upon a recent breakthrough by Recht, Fazel and Parillo (RFP07) and Lee and Bresler (LB09) in three significant ways: #R##N#1) our method (SVP) is significantly simpler to analyze and easier to implement, #R##N#2) we give recovery guarantees under strictly weaker isometry assumptions #R##N#3) we give geometric convergence guarantees for SVP even in presense of noise and, as demonstrated empirically, SVP is significantly faster on real-world and synthetic problems. #R##N#In addition, we address the practically important problem of low-rank matrix completion (MCP), which can be seen as a special case of ARMP. We empirically demonstrate that our algorithm recovers low-rank incoherent matrices from an almost optimal number of uniformly sampled entries. We make partial progress towards proving exact recovery and provide some intuition for the strong performance of SVP applied to matrix completion by showing a more restricted isometry property. Our algorithm outperforms existing methods, such as those of \cite{RFP07,CR08,CT09,CCS08,KOM09,LB09}, for ARMP and the matrix-completion problem by an order of magnitude and is also significantly more robust to noise.

Connected through hyperedge HE-1 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "uniform recovery of fusion frame structured sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"

Connected through hyperedge HE-2 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "fast exact matrix completion a unifying optimization framework"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "fast exact matrix completion with finite samples"
    - "provable inductive matrix completion"
    - "clustering partially observed graphs via convex optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "tensor regression using low rank and sparse tucker decompositions"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "learning from multiway data simple and efficient tensor regression"
    - "low rank tensor recovery via iterative hard thresholding"
    - "lower bounds for sparse recovery"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "most tensor problems are np hard"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "low rank modeling and its applications in image analysis"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-6 (induced by source paper "matrix completion based channel estimation for mmwave communication systems with array inherent impairments"):
  with members:
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"

Connected through hyperedge HE-7 (induced by source paper "connecting weighted automata and recurrent neural networks through spectral learning"):
  with members:
    - "rational recurrences"
    - "explaining black boxes on sequential data using weighted automata"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "compressing recurrent neural network with tensor train"
    - "subregular complexity and deep learning"
    - "long term forecasting using higher order tensor rnns"

Connected through hyperedge HE-8 (induced by source paper "low rank structure of learned representations"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "structured pruning of deep convolutional neural networks"

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)