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: "on deterministic sketching and streaming for sparse recovery and norm estimation"
  Abstract: We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: #R##N#* A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. #R##N#* A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. #R##N#* A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. #R##N#For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems.

Connected through hyperedge HE-1 (induced by source paper "on low risk heavy hitters and sparse recovery schemes"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "clustering high dimensional dynamic data streams"
    - "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 the power of adaptivity in sparse recovery"
    - "1 eps approximate sparse recovery"

Connected through hyperedge HE-2 (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"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-3 (induced by source paper "for all sparse recovery in near optimal time"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "sublinear time measurement optimal sparse recovery for all"
    - "noise resilient group testing limitations and constructions"
    - "approximate sparse recovery optimizing time and measurements"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "tight bounds for set disjointness in the message passing model"):
  with members:
    - "information equals amortized communication"
    - "tight bounds for distributed functional monitoring"
    - "asymptotically optimal lower bounds on the nih multi party information"
    - "multiparty communication complexity of disjointness"

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)