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: "geometric median in nearly linear time"
  Abstract: In this paper we provide faster algorithms for solving the geometric median problem: given $n$ points in $\mathbb{R}^{d}$ compute a point that minimizes the sum of Euclidean distances to the points. This is one of the oldest non-trivial problems in computational geometry yet despite an abundance of research the previous fastest algorithms for computing a $(1+\epsilon)$-approximate geometric median were $O(d\cdot n^{4/3}\epsilon^{-8/3})$ by Chin et. al, $\tilde{O}(d\exp{\epsilon^{-4}\log\epsilon^{-1}})$ by Badoiu et. al, $O(nd+\mathrm{poly}(d,\epsilon^{-1})$ by Feldman and Langberg, and $O((nd)^{O(1)}\log\frac{1}{\epsilon})$ by Parrilo and Sturmfels and Xue and Ye. #R##N#In this paper we show how to compute a $(1+\epsilon)$-approximate geometric median in time $O(nd\log^{3}\frac{1}{\epsilon})$ and $O(d\epsilon^{-2})$. While our $O(d\epsilon^{-2})$ is a fairly straightforward application of stochastic subgradient descent, our $O(nd\log^{3}\frac{1}{\epsilon})$ time algorithm is a novel long step interior point method. To achieve this running time we start with a simple $O((nd)^{O(1)}\log\frac{1}{\epsilon})$ time interior point method and show how to improve it, ultimately building an algorithm that is quite non-standard from the perspective of interior point literature. Our result is one of very few cases we are aware of outperforming traditional interior point theory and the only we are aware of using interior point methods to obtain a nearly linear time algorithm for a canonical optimization problem that traditionally requires superlinear time. We hope our work leads to further improvements in this line of research.

Connected through hyperedge HE-1 (induced by source paper "a rotation invariant framework for deep point cloud analysis"):
  with members:
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "total denoising unsupervised learning of 3d point cloud cleaning"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "pointnetlk robust efficient point cloud registration using pointnet"

Connected through hyperedge HE-2 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"

Connected through hyperedge HE-3 (induced by source paper "real time eeg classification via coresets for bci applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "coresets and sketches"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "randomized composable core sets for distributed submodular maximization"
    - "epsilon kernel coresets for stochastic points"
    - "on the sensitivity of shape fitting problems"
    - "simple and deterministic matrix sketching"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "a rank 1 sketch for matrix multiplicative weights"):
  with members:
    - "introduction to online convex optimization"
    - "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "stability of the lanczos method for matrix function approximation"
    - "faster and simpler width independent parallel algorithms for positive semidefinite programming"

Connected through hyperedge HE-5 (induced by source paper "probabilistic smallest enclosing ball in high dimensions via subgradient sampling"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "stochastic k center and j flat center problems"

Connected through hyperedge HE-6 (induced by source paper "filter pruning via geometric median for deep convolutional neural networks acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "searching for a robust neural architecture in four gpu hours"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "learning to prune filters in convolutional neural networks"
    - "paraphrasing complex network network compression via factor transfer"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "solving linear programs in the current matrix multiplication time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "incomplete nested dissection"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-8 (induced by source paper "data driven clustering via parameterized lloyd s families"):
  with members:
    - "an online hierarchical algorithm for extreme clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "going deeper with convolutions"
    - "deep domain confusion maximizing for domain invariance"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "k means requires exponentially many iterations even in the plane"

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)