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: "streaming symmetric norms via measure concentration"
  Abstract: We characterize the streaming space complexity of every symmetric norm $l$ (a norm on $\mathbb{R}^n$ invariant under sign-flips and coordinate-permutations), by relating this space complexity to the measure-concentration characteristics of $l$. Specifically, we provide nearly matching upper and lower bounds on the space complexity of calculating a $(1\pm\epsilon)$-approximation to the norm of the stream, for every $0<\epsilon\leq 1/2$. (The bounds match up to $poly(\epsilon^{-1} \log n)$ factors.) We further extend those bounds to any large approximation ratio $D\geq 1.1$, showing that the decrease in space complexity is proportional to $D^2$, and that this factor the best possible. All of the bounds depend on the median of $l(x)$ when $x$ is drawn uniformly from the $l_2$ unit sphere. The same median governs many phenomena in high-dimensional spaces, such as large-deviation bounds and the critical dimension in Dvoretzky's Theorem. #R##N#The family of symmetric norms contains several well-studied norms, such as all $l_p$~norms, and indeed we provide a new explanation for the disparity in space complexity between $p\le 2$ and $p>2$. In addition, we apply our general results to easily derive bounds for several norms that were not studied before in the streaming model, including the top-$k$ norm and the $k$-support norm, which was recently employed for machine learning tasks. #R##N#Overall, these results make progress on two outstanding problems in the area of sublinear algorithms (Problems 5 and 30 in~\url{this http URL}).

Connected through hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "tight bounds for the subspace sketch problem with applications"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "regularized weighted low rank approximation"
    - "robust and sample optimal algorithms for psd low rank approximation"
    - "dimensionality reduction for tukey regression"
    - "total least squares regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "faster spectral sparsification in dynamic streams"):
  with members:
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "graph clustering using effective resistance"
    - "edit distance sketching streaming and document exchange"

Connected through hyperedge HE-3 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-4 (induced by source paper "on mean estimation for general norms with statistical queries"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "towards a zero one law for entrywise low rank approximation"
    - "subspace embedding and linear regression with orlicz norm"
    - "on the complexity of learning neural networks"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "approximate near neighbors for general symmetric norms"
    - "a general characterization of the statistical query complexity"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-5 (induced by source paper "towards a zero one law for entrywise low rank approximation"):
  with members:
    - "relative error tensor low rank approximation"
    - "algorithms for ell_p low rank approximation"
    - "low rank approximation with entrywise ell_1 norm error"
    - "input sparsity and hardness for robust subspace approximation"
    - "optimal cur matrix decompositions"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "universal streaming of subset norms"):
  with members:
    - "revisiting frequency moment estimation in random order streams"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "on sketching quadratic forms"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-7 (induced by source paper "approximation algorithms for minimum norm and ordered optimization problems"):
  with members:
    - "constant factor approximation for ordered k median"
    - "approximate near neighbors for general symmetric norms"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "on the configuration lp of the restricted assignment problem"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "santa claus schedules jobs on unrelated machines"
    - "simpler analyses of local search algorithms for facility location"

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"
    - "on approximating functions of the singular values in a stream"
    - "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)