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: "online row sampling"
  Abstract: Finding a small spectral approximation for a tall $n \times d$ matrix $A$ is a fundamental numerical primitive. For a number of reasons, one often seeks an approximation whose rows are sampled from those of $A$. Row sampling improves interpretability, saves space when $A$ is sparse, and preserves row structure, which is especially important, for example, when $A$ represents a graph. #R##N#However, correctly sampling rows from $A$ can be costly when the matrix is large and cannot be stored and processed in memory. Hence, a number of recent publications focus on row sampling in the streaming setting, using little more space than what is required to store the outputted approximation [KL13, KLM+14]. #R##N#Inspired by a growing body of work on online algorithms for machine learning and data analysis, we extend this work to a more restrictive online setting: we read rows of $A$ one by one and immediately decide whether each row should be kept in the spectral approximation or discarded, without ever retracting these decisions. We present an extremely simple algorithm that approximates $A$ up to multiplicative error $\epsilon$ and additive error $\delta$ using $O(d \log d \log(\epsilon||A||_2/\delta)/\epsilon^2)$ online samples, with memory overhead proportional to the cost of storing the spectral approximation. We also present an algorithm that uses $O(d^2$) memory but only requires $O(d\log(\epsilon||A||_2/\delta)/\epsilon^2)$ samples, which we show is optimal. #R##N#Our methods are clean and intuitive, allow for lower memory usage than prior work, and expose new theoretical properties of leverage score based matrix approximation.

Connected through hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression"):
  with members:
    - "robust frequent directions with application in online learning"
    - "efficient frequent directions algorithm for sparse matrices"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-2 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space"):
  with members:
    - "faster spectral sparsification in dynamic streams"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

Connected through hyperedge HE-3 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "distributed edge connectivity in sublinear time"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-4 (induced by source paper "sketched mindist"):
  with members:
    - "scalable spatial scan statistics for trajectories"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "simple distances for trajectories via landmarks"
    - "relative error rkhs embeddings for gaussian kernels"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "on the sensitivity of shape fitting problems"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "nystrom landmark sampling and regularized christoffel functions"):
  with members:
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "relating leverage scores and density using regularized christoffel functions"
    - "efficient sampling for k determinantal point processes"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-6 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "lectures on randomized numerical linear algebra"
    - "submodular optimization over sliding windows"
    - "sketches for matrix norms faster smaller and more general"
    - "submodular maximization over sliding windows"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"
    - "sparsified cholesky solvers for sdd linear systems"

Connected through hyperedge HE-7 (induced by source paper "communication optimal distributed dynamic graph clustering"):
  with members:
    - "communication optimal distributed clustering"
    - "partitioning well clustered graphs spectral clustering works"
    - "iterative row sampling"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Connected through hyperedge HE-8 (induced by source paper "simple distances for trajectories via landmarks"):
  with members:
    - "sketched mindist"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "on the sensitivity of shape fitting problems"
    - "an improved approximation algorithm for the column subset selection problem"

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)