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: "clustering high dimensional dynamic data streams"
  Abstract: We present data streaming algorithms for the $k$-median problem in high-dimensional dynamic geometric data streams, i.e. streams allowing both insertions and deletions of points from a discrete Euclidean space $\{1, 2, \ldots \Delta\}^d$. Our algorithms use $k \epsilon^{-2} poly(d \log \Delta)$ space/time and maintain with high probability a small weighted set of points (a coreset) such that for every set of $k$ centers the cost of the coreset $(1+\epsilon)$-approximates the cost of the streamed point set. We also provide algorithms that guarantee only positive weights in the coreset with additional logarithmic factors in the space and time complexities. We can use this positively-weighted coreset to compute a $(1+\epsilon)$-approximation for the $k$-median problem by any efficient offline $k$-median algorithm. All previous algorithms for computing a $(1+\epsilon)$-approximation for the $k$-median problem over dynamic data streams required space and time exponential in $d$. Our algorithms can be generalized to metric spaces of bounded doubling dimension.

Connected through hyperedge HE-1 (induced by source paper "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "constant factor fpt approximation for capacitated k median"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "new frameworks for offline and streaming coreset constructions"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-2 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"

Connected through hyperedge HE-3 (induced by source paper "turning big data into tiny data constant size coresets for k means pca and projective clustering"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "new frameworks for offline and streaming coreset constructions"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal approximate matrix product in terms of stable rank"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "on the sensitivity of shape fitting problems"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal dynamic k means clustering for high dimensional data"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "relative error tensor low rank approximation"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields a ptas for k means in doubling metrics"
    - "low rank approximation with entrywise ell_1 norm error"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "heavy hitters via cluster preserving clustering"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-5 (induced by source paper "varepsilon coresets for clustering with outliers in doubling metrics"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "coresets and sketches"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields a ptas for k means in doubling metrics"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "scalable secure computation of statistical functions with applications to k nearest neighbors"):
  with members:
    - "secure search on the cloud via coresets and sketches"
    - "coresets and sketches"
    - "a secure database system using homomorphic encryption schemes"

Connected through hyperedge HE-7 (induced by source paper "finding frequent entities in continuous data"):
  with members:
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "on low risk heavy hitters and sparse recovery schemes"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "heavy hitters via cluster preserving clustering"
    - "restricted isometry property for general p norms"
    - "for all sparse recovery in near optimal time"
    - "compressive sensing with local geometric features"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "on the power of adaptivity in sparse recovery"
    - "1 eps approximate sparse recovery"

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)