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: "for all sparse recovery in near optimal time"
  Abstract: An approximate sparse recovery system in $\ell_1$ norm consists of parameters $k$, $\epsilon$, $N$, an $m$-by-$N$ measurement $\Phi$, and a recovery algorithm, $\mathcal{R}$. Given a vector, $\mathbf{x}$, the system approximates $x$ by $\widehat{\mathbf{x}} = \mathcal{R}(\Phi\mathbf{x})$, which must satisfy $\|\widehat{\mathbf{x}}-\mathbf{x}\|_1 \leq (1+\epsilon)\|\mathbf{x}-\mathbf{x}_k\|_1$. We consider the 'for all' model, in which a single matrix $\Phi$, possibly 'constructed' non-explicitly using the probabilistic method, is used for all signals $\mathbf{x}$. The best existing sublinear algorithm by Porat and Strauss (SODA'12) uses $O(\epsilon^{-3} k\log(N/k))$ measurements and runs in time $O(k^{1-\alpha}N^\alpha)$ for any constant $\alpha > 0$. #R##N#In this paper, we improve the number of measurements to $O(\epsilon^{-2} k \log(N/k))$, matching the best existing upper bound (attained by super-linear algorithms), and the runtime to $O(k^{1+\beta}\textrm{poly}(\log N,1/\epsilon))$, with a modest restriction that $\epsilon \leq (\log k/\log N)^{\gamma}$, for any constants $\beta,\gamma > 0$. When $k\leq \log^c N$ for some $c>0$, the runtime is reduced to $O(k\textrm{poly}(N,1/\epsilon))$. With no restrictions on $\epsilon$, we have an approximation recovery system with $m = O(k/\epsilon \log(N/k)((\log N/\log k)^\gamma + 1/\epsilon))$ measurements.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal sparse polynomial multiplication"):
  with members:
    - "what can and can t we do with sparse polynomials"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "nearly optimal sparse fourier transform"
    - "chunky and equal spaced polynomial multiplication"
    - "approximate sparse recovery optimizing time and measurements"

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"
    - "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"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "sublinear time algorithms for compressive phase retrieval"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "predicting positive and negative links with noisy queries theory practice"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"

Connected through hyperedge HE-5 (induced by source paper "heavy hitters and the structure of local privacy"):
  with members:
    - "locally private k means clustering"
    - "differentially private k means with constant multiplicative error"
    - "practical locally private heavy hitters"
    - "clustering algorithms for the centralized and local models"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "max information differential privacy and post selection hypothesis testing"
    - "heavy hitters via cluster preserving clustering"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "one bit expandersketch for one bit compressed sensing"):
  with members:
    - "improved bounds for universal one bit compressive sensing"
    - "on fast decoding of high dimensional signals from one bit measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "heavy hitters via cluster preserving clustering"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-8 (induced by source paper "heavy hitters via cluster preserving clustering"):
  with members:
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "beating countsketch for heavy hitters in insertion streams"
    - "linear time list recovery of high rate expander codes"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "flow based algorithms for local graph clustering"

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)