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 "compressed matrix multiplication"):
  Abstract: Motivated by the problems of computing sample covariance matrices, and of transforming a collection of vectors to a basis where they are sparse, we present a simple algorithm that computes an approximation of the product of two n-by-n real matrices A and B. Let ||AB||_F denote the Frobenius norm of AB, and b be a parameter determining the time/accuracy trade-off. Given 2-wise independent hash functions $_1,h_2: [n] -> [b], and s_1,s_2: [n] -> {-1,+1} the algorithm works by first "compressing" the matrix product into the polynomial p(x) = sum_{k=1}^n (sum_{i=1}^n A_{ik} s_1(i) x^{h_1(i)}) (sum_{j=1}^n B_{kj} s_2(j) x^{h_2(j)}) #R##N#Using FFT for polynomial multiplication, we can compute c_0,...,c_{b-1} such that sum_i c_i x^i = (p(x) mod x^b) + (p(x) div x^b) in time \~O(n^2+ n b). #R##N#An unbiased estimator of (AB)_{ij} with variance at most ||AB||_F^2 / b can then be computed as: #R##N#C_{ij} = s_1(i) s_2(j) c_{(h_1(i)+h_2(j)) mod b. #R##N#Our approach also leads to an algorithm for computing AB exactly, whp., in time \~O(N + nb) in the case where A and B have at most N nonzero entries, and AB has at most b nonzero entries. #R##N#Also, we use error-correcting codes in a novel way to recover significant entries of AB in near-linear time.

Includes members:
  Member "faster algorithms for rectangular matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "deterministic algorithms for skewed matrix products":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster neural network training with approximate tensor operations")
  Member "algorithmic and statistical perspectives on large scale data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting the nystrom method for improved large scale machine learning")
  Member "better size estimation for sparse matrix products":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "the power of simple tabulation hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "ams without 4 wise independence on product domains":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "oblivious sketching of high degree polynomial kernels")

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)