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: "on approximating functions of the singular values in a stream"
  Abstract: For any real number $p > 0$, we nearly completely characterize the space complexity of estimating $\|A\|_p^p = \sum_{i=1}^n \sigma_i^p$ for $n \times n$ matrices $A$ in which each row and each column has $O(1)$ non-zero entries and whose entries are presented one at a time in a data stream model. Here the $\sigma_i$ are the singular values of $A$, and when $p \geq 1$, $\|A\|_p^p$ is the $p$-th power of the Schatten $p$-norm. We show that when $p$ is not an even integer, to obtain a $(1+\epsilon)$-approximation to $\|A\|_p^p$ with constant probability, any $1$-pass algorithm requires $n^{1-g(\epsilon)}$ bits of space, where $g(\epsilon) \rightarrow 0$ as $\epsilon \rightarrow 0$ and $\epsilon > 0$ is a constant independent of $n$. However, when $p$ is an even integer, we give an upper bound of $n^{1-2/p} \textrm{poly}(\epsilon^{-1}\log n)$ bits of space, which holds even in the turnstile data stream model. The latter is optimal up to $\textrm{poly}(\epsilon^{-1} \log n)$ factors. #R##N#Our results considerably strengthen lower bounds in previous work for arbitrary (not necessarily sparse) matrices $A$: the previous best lower bound was $\Omega(\log n)$ for $p\in (0,1)$, $\Omega(n^{1/p-1/2}/\log n)$ for $p\in [1,2)$ and $\Omega(n^{1-2/p})$ for $p\in (2,\infty)$. We note for $p \in (2, \infty)$, while our lower bound for even integers is the same, for other $p$ in this range our lower bound is $n^{1-g(\epsilon)}$, which is considerably stronger than the previous $n^{1-2/p}$ for small enough constant $\epsilon > 0$. We obtain similar near-linear lower bounds for Ky-Fan norms, SVD entropy, eigenvalue shrinkers, and M-estimators, many of which could have been solvable in logarithmic space prior to our work.

Connected through hyperedge HE-1 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "approximating the spectrum of a graph"
    - "embeddings of schatten norms with applications to data streams"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "spectrum estimation from samples"
    - "matrix norms in data streams faster multi pass and row order"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"

Connected through hyperedge HE-2 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "on estimating maximum matching size in graph streams"
    - "embeddings of schatten norms with applications to data streams"
    - "matrix norms in data streams faster multi pass and row order"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-3 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "on the multiparty communication complexity of testing triangle freeness"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-4 (induced by source paper "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "embeddings of schatten norms with applications to data streams"
    - "the dictionary testing problem"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "sketching as a tool for numerical linear algebra"
    - "relations among some low rank subspace recovery models"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-6 (induced by source paper "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-7 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-8 (induced by source paper "embeddings of schatten norms with applications to data streams"):
  with members:
    - "sketches for matrix norms faster smaller and more general"
    - "spectrum estimation from samples"
    - "streaming symmetric norms via measure concentration"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "a derandomized sparse johnson lindenstrauss transform"

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)