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 "stronger l2 l2 compressed sensing without iterating"):
  Abstract: We consider the extensively studied problem of $\ell_2/\ell_2$ compressed sensing. The main contribution of our work is an improvement over [Gilbert, Li, Porat and Strauss, STOC 2010] with faster decoding time and significantly smaller column sparsity, answering two open questions of the aforementioned work. #R##N#Previous work on sublinear-time compressed sensing employed an iterative procedure, recovering the heavy coordinates in phases. We completely depart from that framework, and give the first sublinear-time $\ell_2/\ell_2$ scheme which achieves the optimal number of measurements without iterating; this new approach is the key step to our progress. Towards that, we satisfy the $\ell_2/\ell_2$ guarantee by exploiting the heaviness of coordinates in a way that was not exploited in previous work. Via our techniques we obtain improved results for various sparse recovery tasks, and indicate possible further applications to problems in the field, to which the aforementioned iterative procedure creates significant obstructions.

Includes members:
  Member "dimension independent sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless")
  Member "solving linear programs in the current matrix multiplication time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "improved algorithms for adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "querying a matrix through matrix vector products")
  Member "sublinear time algorithms for compressive phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved algorithms for adaptive compressed sensing")
  Member "one bit expandersketch for one bit compressed sensing":
    also connected through other hyperedges:
      (none)
  Member "an adaptive sublinear time block sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "almost optimal phaseless compressed sensing with sublinear decoding time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sublinear time algorithms for compressive phase retrieval")
  Member "sample efficient estimation and recovery in sparse fft via isolation on average":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privacy preserving learning via deep net pruning")

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)