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: "near optimal sketching of low rank tensor regression"
  Abstract: We study the least squares regression problem \begin{align*} \min_{\Theta \in \mathcal{S}_{\odot D,R}} \|A\Theta-b\|_2, \end{align*} where $\mathcal{S}_{\odot D,R}$ is the set of $\Theta$ for which $\Theta = \sum_{r=1}^{R} \theta_1^{(r)} \circ \cdots \circ \theta_D^{(r)}$ for vectors $\theta_d^{(r)} \in \mathbb{R}^{p_d}$ for all $r \in [R]$ and $d \in [D]$, and $\circ$ denotes the outer product of vectors. That is, $\Theta$ is a low-dimensional, low-rank tensor. This is motivated by the fact that the number of parameters in $\Theta$ is only $R \cdot \sum_{d=1}^D p_d$, which is significantly smaller than the $\prod_{d=1}^{D} p_d$ number of parameters in ordinary least squares regression. We consider the above CP decomposition model of tensors $\Theta$, as well as the Tucker decomposition. For both models we show how to apply data dimensionality reduction techniques based on {\it sparse} random projections $\Phi \in \mathbb{R}^{m \times n}$, with $m \ll n$, to reduce the problem to a much smaller problem $\min_{\Theta} \|\Phi A \Theta - \Phi b\|_2$, for which if $\Theta'$ is a near-optimum to the smaller problem, then it is also a near optimum to the original problem. We obtain significantly smaller dimension and sparsity in $\Phi$ than is possible for ordinary least squares regression, and we also provide a number of numerical simulations supporting our theory.

Connected through hyperedge HE-1 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "multiscale analysis for higher order tensors"
    - "generating long term trajectories using deep hierarchical networks"
    - "tensor regression meets gaussian processes"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "tensorly tensor learning in python"
    - "why should i trust you explaining the predictions of any classifier"
    - "parallel tensor compression for large scale scientific data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "solving linear programs in the current matrix multiplication time"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"

Connected through hyperedge HE-3 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "total least squares regression in input sparsity time"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "sensitivity sampling over dynamic geometric data streams with applications to k clustering"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "sketching for kronecker product regression and p splines"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-4 (induced by source paper "total least squares regression in input sparsity time"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "dimensionality reduction for tukey regression"
    - "solving linear programs in the current matrix multiplication time"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-5 (induced by source paper "an empirical evaluation of sketched svd and its application to leverage score ordering"):
  with members:
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "learning factorized multimodal representations"
    - "sketching for kronecker product regression and p splines"
    - "practical and optimal lsh for angular distance"
    - "distributed kernel principal component analysis"
    - "speech recognition with deep recurrent neural networks"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-6 (induced by source paper "tensor embedding a supervised framework for human behavioral data mining and prediction"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "spectral sparsification of simplicial complexes for clustering and label propagation"):
  with members:
    - "polynomial sized topological approximations using the permutahedron"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pagerank beyond the web"
    - "graph induced complex on point data"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"
    - "fast approximation of matrix coherence and statistical leverage"

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)