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: "efficient sketches for the set query problem"
  Abstract: We develop an algorithm for estimating the values of a vector x in R^n over a support S of size k from a randomized sparse binary linear sketch Ax of size O(k). Given Ax and S, we can recover x' with ||x' - x_S||_2 <= eps ||x - x_S||_2 with probability at least 1 - k^{-\Omega(1)}. The recovery takes O(k) time. #R##N#While interesting in its own right, this primitive also has a number of applications. For example, we can: #R##N#1. Improve the linear k-sparse recovery of heavy hitters in Zipfian distributions with O(k log n) space from a (1+eps) approximation to a (1 + o(1)) approximation, giving the first such approximation in O(k log n) space when k <= O(n^{1-eps}). #R##N#2. Recover block-sparse vectors with O(k) space and a (1+eps) approximation. Previous algorithms required either omega(k) space or omega(1) approximation.

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 "dynamic graph stream algorithms in o n space"):
  with members:
    - "correlation clustering in data streams"
    - "estimating graph parameters from random order streams"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "densest subgraph in dynamic graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"

Connected through hyperedge HE-3 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing"):
  with members:
    - "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"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-4 (induced by source paper "beating countsketch for heavy hitters in insertion streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "tracking the frequency moments at all times"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "approximating large frequency moments with pick and drop sampling"
    - "low rank approximation and regression in input sparsity time"
    - "improved concentration bounds for count sketch"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"

Connected through hyperedge HE-5 (induced by source paper "sho fa robust compressive sensing with order optimal complexity measurements and bits"):
  with members:
    - "a family of optimal locally recoverable codes"
    - "biff bloom filter codes fast error correction for large data sets"
    - "fast matrix rank algorithms and applications"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "one bit compressed sensing by linear programming"
    - "optimal phase transitions in compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "1 eps approximate sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "improved concentration bounds for count sketch"):
  with members:
    - "on the power of adaptivity in sparse recovery"
    - "1 eps approximate sparse recovery"
    - "model based compressive sensing"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-7 (induced by source paper "k median clustering model based compressive sensing and sparse recovery for earth mover distance"):
  with members:
    - "lower bounds for sparse recovery"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-8 (induced by source paper "invertible bloom lookup tables"):
  with members:
    - "privacy preserving access of outsourced data via oblivious ram simulation"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

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)