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: "lower bounds for adaptive sparse recovery"
  Abstract: We give lower bounds for the problem of stable sparse recovery from /adaptive/ linear measurements. In this problem, one would like to estimate a vector $x \in \R^n$ from $m$ linear measurements $A_1x,..., A_mx$. One may choose each vector $A_i$ based on $A_1x,..., A_{i-1}x$, and must output $x*$ satisfying |x* - x|_p \leq (1 + \epsilon) \min_{k\text{-sparse} x'} |x - x'|_p with probability at least $1-\delta>2/3$, for some $p \in \{1,2\}$. For $p=2$, it was recently shown that this is possible with $m = O(\frac{1}{\epsilon}k \log \log (n/k))$, while nonadaptively it requires $\Theta(\frac{1}{\epsilon}k \log (n/k))$. It is also known that even adaptively, it takes $m = \Omega(k/\epsilon)$ for $p = 2$. For $p = 1$, there is a non-adaptive upper bound of $\tilde{O}(\frac{1}{\sqrt{\epsilon}} k\log n)$. We show: #R##N#* For $p=2$, $m = \Omega(\log \log n)$. This is tight for $k = O(1)$ and constant $\epsilon$, and shows that the $\log \log n$ dependence is correct. #R##N#* If the measurement vectors are chosen in $R$ "rounds", then $m = \Omega(R \log^{1/R} n)$. For constant $\epsilon$, this matches the previously known upper bound up to an O(1) factor in $R$. #R##N#* For $p=1$, $m = \Omega(k/(\sqrt{\epsilon} \cdot \log k/\epsilon))$. This shows that adaptivity cannot improve more than logarithmic factors, providing the analog of the $m = \Omega(k/\epsilon)$ bound for $p = 2$.

Connected through hyperedge HE-1 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "on estimating maximum matching size in graph streams"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-2 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-3 (induced by source paper "on the randomized complexity of minimizing a convex quadratic function"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "mutual information in rank one matrix estimation"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-4 (induced by source paper "batch sparse recovery or how to leverage the average sparsity"):
  with members:
    - "when distributed computation is communication expensive"
    - "on the power of adaptivity in sparse recovery"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-5 (induced by source paper "improved algorithms for adaptive compressed sensing"):
  with members:
    - "sublinear time algorithms for compressive phase retrieval"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "on the power of adaptivity in sparse recovery"
    - "1 eps approximate sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"
    - "sequential adaptive compressed sampling via huffman codes"

Connected through hyperedge HE-6 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "compressive principal component pursuit"
    - "compressive binary search"
    - "sketched svd recovering spectral features from compressive measurements"
    - "algorithms and hardness for robust subspace recovery"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"):
  with members:
    - "near linear time approximation schemes for uncapacitated and capacitated b matching problems in nonbipartite graphs"
    - "a local computation approximation scheme to maximum matching"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "weighted matching in the semi streaming model"

Connected through hyperedge HE-8 (induced by source paper "near optimal adaptive compressed sensing"):
  with members:
    - "the sample complexity of search over multiple populations"
    - "near optimal compressive binary search"
    - "boolean compressed sensing and noisy group testing"
    - "compressive binary search"
    - "sequential testing for sparse recovery"
    - "convex approaches to model wavelet sparsity patterns"
    - "on the limits of sequential testing in high dimensions"
    - "information theoretic bounds for compressed sensing"

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)