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: "sparse vector distributions and recovery from compressed sensing"
  Abstract: It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal.

Connected through hyperedge HE-1 (induced by source paper "relaxed recovery conditions for omp ols by exploiting both coherence and decay"):
  with members:
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "near optimal binary compressed sensing matrix"):
  with members:
    - "ldpc codes for compressed sensing"
    - "sparse reconstruction via the reed muller sieve"
    - "compressed sensing how sharp is the restricted isometry property"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"

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)