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: "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
  Abstract: We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m x n matrices A such that for any x, given Ax, we can recover a k-sparse approximation to x under the EMD distance. One construction yields m = O(k log(n/k)) and a 1 + epsilon approximation factor, which matches the best achievable bound for other error measures, such as the L_1 norm. Our algorithms are obtained by exploiting novel connections to other problems and areas, such as streaming algorithms for k-median clustering and model-based compressive sensing. We also provide novel algorithms and results for the latter problems.

Connected through hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-2 (induced by source paper "clustering high dimensional dynamic data streams"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "heavy hitters via cluster preserving clustering"
    - "a unified framework for approximating and clustering data"
    - "deterministic sampling and range counting in geometric data streams"

Connected through hyperedge HE-3 (induced by source paper "quantitative recovery conditions for tree based compressed sensing"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "approximation algorithms for model based compressive sensing"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "convex block sparse linear regression with expanders provably"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-5 (induced by source paper "a unified approach for clustering problems on sliding windows"):
  with members:
    - "element distinctness frequency moments and sliding windows"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "on model based rip 1 matrices"):
  with members:
    - "lower bounds for sparse recovery"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal 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)