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: "approximate near neighbors for general symmetric norms"
  Abstract: We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation. Specifically, for every $n$, $d = n^{o(1)}$, and every $d$-dimensional symmetric norm $\|\cdot\|$, there exists a data structure for $\mathrm{poly}(\log \log n)$-approximate nearest neighbor search over $\|\cdot\|$ for $n$-point datasets achieving $n^{o(1)}$ query time and $n^{1+o(1)}$ space. The main technical ingredient of the algorithm is a low-distortion embedding of a symmetric norm into a low-dimensional iterated product of top-$k$ norms. #R##N#We also show that our techniques cannot be extended to general norms.

Connected through hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"

Connected through hyperedge HE-2 (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"
    - "input sparsity and hardness for robust subspace approximation"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-3 (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"
    - "a general characterization of the statistical query complexity"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "streaming symmetric norms via measure concentration"

Connected through hyperedge HE-4 (induced by source paper "near neighbor preserving dimension reduction for doubling subsets of ell_1"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "approximate greedy clustering and distance selection for graph metrics"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "randomized embeddings with slack and high dimensional approximate nearest neighbor"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-5 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimal terminal dimensionality reduction in euclidean space"
    - "optimality of the johnson lindenstrauss lemma"
    - "prioritized metric structures and embedding"
    - "sketching and embedding are equivalent for norms"
    - "constant factor approximation for balanced cut in the pie model"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-6 (induced by source paper "approximation algorithms for minimum norm and ordered optimization problems"):
  with members:
    - "constant factor approximation for ordered k median"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "on the configuration lp of the restricted assignment problem"
    - "streaming symmetric norms via measure concentration"
    - "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-7 (induced by source paper "subspace embedding and linear regression with orlicz norm"):
  with members:
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "algorithms for ell_p low rank approximation"
    - "active regression with adaptive huber loss"
    - "low rank approximation with entrywise ell_1 norm error"
    - "robust regression via hard thresholding"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"

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)