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: "sketching for large scale learning of mixture models"
  Abstract: Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a "compressive learning" framework where we estimate model parameters from a sketch of the training data. This sketch is a collection of generalized moments of the underlying probability distribution of the data. It can be computed in a single pass on the training set, and is easily computable on streams or distributed datasets. The proposed framework shares similarities with compressive sensing, which aims at drastically reducing the dimension of high-dimensional signals while preserving the ability to reconstruct them. To perform the estimation task, we derive an iterative algorithm analogous to sparse reconstruction algorithms in the context of linear inverse problems. We exemplify our framework with the compressive estimation of a Gaussian Mixture Model (GMM), providing heuristics on the choice of the sketching procedure and theoretical guarantees of reconstruction. We experimentally show on synthetic data that the proposed algorithm yields results comparable to the classical Expectation-Maximization (EM) technique while requiring significantly less memory and fewer computations when the number of database elements is large. We further demonstrate the potential of the approach on real large-scale data (over 10 8 training samples) for the task of model-based speaker verification. Finally, we draw some connections between the proposed framework and approximate Hilbert space embedding of probability distributions using random features. We show that the proposed sketching operator can be seen as an innovative method to design translation-invariant kernels adapted to the analysis of GMMs. We also use this theoretical framework to derive information preservation guarantees, in the spirit of infinite-dimensional compressive sensing.

Connected through hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-2 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-3 (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"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "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-4 (induced by source paper "instance optimal decoding and the restricted isometry property"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "representation and coding of signal geometry"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing in hilbert spaces"):
  with members:
    - "compressive k means"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-6 (induced by source paper "sketched clustering via hybrid approximate message passing"):
  with members:
    - "quantized compressive k means"
    - "hybrid approximate message passing"
    - "sparse multinomial logistic regression via approximate message passing"
    - "compressive k means"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-7 (induced by source paper "compressive k means"):
  with members:
    - "greedy column subset selection new bounds and distributed algorithms"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a unified framework for approximating and clustering data"
    - "random projections for k means clustering"

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)