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 "new constructions of rip matrices with fast multiplication and fewer rows"):
  Abstract: In compressed sensing, the "restricted isometry property" (RIP) is a sufficient condition for the efficient reconstruction of a nearly k-sparse vector x in C^d from m linear measurements Phi x. It is desirable for m to be small, and for Phi to support fast matrix-vector multiplication. In this work, we give a randomized construction of RIP matrices Phi in C^{m x d}, preserving the L_2 norms of all k-sparse vectors with distortion 1+eps, where the matrix-vector multiply Phi x can be computed in nearly linear time. The number of rows m is on the order of eps^{-2}klog dlog^2(klog d). Previous analyses of constructions of RIP matrices supporting fast matrix-vector multiplies, such as the sampled discrete Fourier matrix, required m to be larger by roughly a log k factor. #R##N#Supporting fast matrix-vector multiplication is useful for iterative recovery algorithms which repeatedly multiply by Phi or Phi^*. Furthermore, our construction, together with a connection between RIP matrices and the Johnson-Lindenstrauss lemma in [Krahmer-Ward, SIAM. J. Math. Anal. 2011], implies fast Johnson-Lindenstrauss embeddings with asymptotically fewer rows than previously known. #R##N#Our approach is a simple twist on previous constructions. Rather than choosing the rows for the embedding matrix to be rows sampled from some larger structured matrix (such as the discrete Fourier transform or a random circulant matrix), we instead choose each row of the embedding matrix to be a linear combination of a small number of rows of the original matrix, with random sign flips as coefficients. The main tool in our analysis is a recent bound for the supremum of certain types of Rademacher chaos processes in [Krahmer-Mendelson-Rauhut, arXiv:1207.0235].

Includes members:
  Member "fast and rip optimal transforms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "sparsity lower bounds for dimensionality reducing maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "osnap faster numerical linear algebra algorithms via sparser subspace embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "restricted isometry of fourier matrices and list decodability of random linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "stable manifold embeddings with structured random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "low distortion subspace embeddings in input sparsity time and applications to robust linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "querying a matrix through matrix vector products")

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)