human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "querying a matrix through matrix vector products")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "block sparsity coherence and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo")
  Member "condition numbers of gaussian random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")

Question: Please tell me which class the source paper 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)