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: "optimal approximate matrix product in terms of stable rank"
  Abstract: We prove, using the subspace embedding guarantee in a black box way, that one can achieve the spectral norm guarantee for approximate matrix multiplication with a dimensionality-reducing map having $m = O(\tilde{r}/\varepsilon^2)$ rows. Here $\tilde{r}$ is the maximum stable rank, i.e. squared ratio of Frobenius and operator norms, of the two matrices being multiplied. This is a quantitative improvement over previous work of [MZ11, KVZ14], and is also optimal for any oblivious dimensionality-reducing map. Furthermore, due to the black box reliance on the subspace embedding property in our proofs, our theorem can be applied to a much more general class of sketching matrices than what was known before, in addition to achieving better bounds. For example, one can apply our theorem to efficient subspace embeddings such as the Subsampled Randomized Hadamard Transform or sparse subspace embeddings, or even with subspace embedding constructions that may be developed in the future. #R##N#Our main theorem, via connections with spectral error matrix multiplication shown in prior work, implies quantitative improvements for approximate least squares regression and low rank approximation. Our main result has also already been applied to improve dimensionality reduction guarantees for $k$-means clustering [CEMMP14], and implies new results for nonparametric regression [YPW15]. #R##N#We also separately point out that the proof of the "BSS" deterministic row-sampling result of [BSS12] can be modified to show that for any matrices $A, B$ of stable rank at most $\tilde{r}$, one can achieve the spectral norm guarantee for approximate matrix multiplication of $A^T B$ by deterministically sampling $O(\tilde{r}/\varepsilon^2)$ rows that can be found in polynomial time. The original result of [BSS12] was for rank instead of stable rank. Our observation leads to a stronger version of a main theorem of [KMST10].

Connected through hyperedge HE-1 (induced by source paper "speeding up linear programming using randomized linear algebra"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "solving linear programs with sqrt rank linear system solves"
    - "solving linear programs in the current matrix multiplication time"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "sparse coresets for svd on infinite streams"):
  with members:
    - "tight sensitivity bounds for smaller coresets"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "fast approximation of matrix coherence and statistical leverage"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "sublinear time numerical linear algebra for structured matrices"
    - "sample optimal low rank approximation of distance matrices"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "sublinear time low rank approximation of distance matrices"
    - "sublinear time low rank approximation of positive semidefinite matrices"

Connected through hyperedge HE-4 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-5 (induced by source paper "large scale beamforming for massive mimo via randomized sketching"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "group sparse beamforming for green cloud ran"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-6 (induced by source paper "random sampling for distributed coded matrix multiplication"):
  with members:
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "optimal cur matrix decompositions"
    - "low rank approximation and regression in input sparsity time"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-7 (induced by source paper "almost optimal tensor sketch"):
  with members:
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "fastfood approximate kernel expansions in loglinear time"
    - "sparser johnson lindenstrauss transforms"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "feature hashing for large scale multitask learning"
    - "ams without 4 wise independence on product domains"

Connected through hyperedge HE-8 (induced by source paper "space lower bounds for linear prediction"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "on communication complexity of classification problems"
    - "matrix norms in data streams faster multi pass and row order"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on the communication complexity of linear algebraic problems in the message passing model"

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.DS(Data Structures and Algorithms)