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: "embeddings of schatten norms with applications to data streams"
  Abstract: Given an $n \times d$ matrix $A$, its Schatten-$p$ norm, $p \geq 1$, is defined as $\|A\|_p = \left (\sum_{i=1}^{\textrm{rank}(A)}\sigma_i(A)^p \right )^{1/p}$, where $\sigma_i(A)$ is the $i$-th largest singular value of $A$. These norms have been studied in functional analysis in the context of non-commutative $\ell_p$-spaces, and recently in data stream and linear sketching models of computation. Basic questions on the relations between these norms, such as their embeddability, are still open. Specifically, given a set of matrices $A^1, \ldots, A^{\operatorname{poly}(nd)} \in \mathbb{R}^{n \times d}$, suppose we want to construct a linear map $L$ such that $L(A^i) \in \mathbb{R}^{n' \times d'}$ for each $i$, where $n' \leq n$ and $d' \leq d$, and further, $\|A^i\|_p \leq \|L(A^i)\|_q \leq D_{p,q} \|A^i\|_p$ for a given approximation factor $D_{p,q}$ and real number $q \geq 1$. Then how large do $n'$ and $d'$ need to be as a function of $D_{p,q}$? #R##N#We nearly resolve this question for every $p, q \geq 1$, for the case where $L(A^i)$ can be expressed as $R \cdot A^i \cdot S$, where $R$ and $S$ are arbitrary matrices that are allowed to depend on $A^1, \ldots, A^t$, that is, $L(A^i)$ can be implemented by left and right matrix multiplication. Namely, for every $p, q \geq 1$, we provide nearly matching upper and lower bounds on the size of $n'$ and $d'$ as a function of $D_{p,q}$. Importantly, our upper bounds are {\it oblivious}, meaning that $R$ and $S$ do not depend on the $A^i$, while our lower bounds hold even if $R$ and $S$ depend on the $A^i$. As an application of our upper bounds, we answer a recent open question of Blasiok et al. about space-approximation trade-offs for the Schatten $1$-norm, showing in a data stream it is possible to estimate the Schatten-$1$ norm up to a factor of $D \geq 1$ using $\tilde{O}(\min(n,d)^2/D^4)$ space.

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"
    - "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"
    - "on approximating functions of the singular values in a stream"
    - "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"
    - "on approximating functions of the singular values in a stream"
    - "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 "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "on approximating functions of the singular values in a stream"
    - "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-4 (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"
    - "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"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"

Connected through hyperedge HE-5 (induced by source paper "sketches for matrix norms faster smaller and more general"):
  with members:
    - "how to fake multiply by a gaussian matrix"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "distributed estimation of generalized matrix rank efficient algorithms and lower bounds"
    - "optimal approximate matrix product in terms of stable rank"
    - "streaming symmetric norms via measure concentration"
    - "sketching cuts in graphs and hypergraphs"
    - "sketching and embedding are equivalent for norms"

Connected through hyperedge HE-6 (induced by source paper "reliable crowdsourcing under the generalized dawid skene model"):
  with members:
    - "crowdsourcing with sparsely interacting workers"
    - "spectrum estimation from samples"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "spectral redemption in clustering sparse networks"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "matrix completion from a few entries"

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)