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: "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"
  Abstract: This paper develops a new method for recovering m-sparse signals that is simultaneously uniform and quick. We present a reconstruction algorithm whose run time, O(m log^2(m) log^2(d)), is sublinear in the length d of the signal. The reconstruction error is within a logarithmic factor (in m) of the optimal m-term approximation error in l_1. In particular, the algorithm recovers m-sparse signals perfectly and noisy signals are recovered with polylogarithmic distortion. Our algorithm makes O(m log^2 (d)) measurements, which is within a logarithmic factor of optimal. We also present a small-space implementation of the algorithm. These sketching techniques and the corresponding reconstruction algorithms provide an algorithmic dimension reduction in the l_1 norm. In particular, vectors of support m in dimension d can be linearly embedded into O(m log^2 d) dimensions with polylogarithmic distortion. We can reconstruct a vector from its low-dimensional sketch in time O(m log^2(m) log^2(d)). Furthermore, this reconstruction is stable and robust under small perturbations.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "simple codes and sparse recovery with fast decoding"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "cross sender bit mixing coding"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-3 (induced by source paper "simple codes and sparse recovery with fast decoding"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "for all sparse recovery in near optimal time"
    - "noise resilient group testing limitations and constructions"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (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 deterministic sketching and streaming for sparse recovery and norm estimation"
    - "on the power of adaptivity in sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "fast and robust compressive phase retrieval with sparse graph codes"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-6 (induced by source paper "amon an open source architecture for online monitoring statistical analysis and forensics of multi gigabit streams"):
  with members:
    - "hashing pursuit for online identification of heavy hitters in high speed network streams"
    - "sublinear time measurement optimal sparse recovery for all"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-7 (induced by source paper "for all sparse recovery in near optimal time"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "hashing pursuit for online identification of heavy hitters in high speed network streams"):
  with members:
    - "sublinear time measurement optimal sparse recovery for all"
    - "approximate sparse recovery optimizing time and measurements"

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)