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 "the dictionary testing problem"):
  Abstract: The dictionary learning (or sparse coding) problem plays an important role in signal processing, neuroscience, statistics and machine learning. Given a collection of vectors, the goal is to learn a basis with respect to which all the given vectors have a sparse representation. In this work, we study the testing analogue of this problem. #R##N#Roughly speaking, the dictionary testing problem is to decide whether a given collection of vectors can be approximately transformed into sparse vectors. More precisely, given as input vectors $ y_1, \ldots, y_p \in R^d$, we want to distinguish between the following two cases (for some choice of parameters): #R##N#(i) YES case: There exist a matrix $A \in R^{d \times m}$ satisfying RIP and $k$-sparse unit vectors $x_1, \ldots, x_p \in R^m$ such that $y_i = A x_i$ for all $i$. #R##N#(ii) NO case: There does not exist $k'$-sparse unit vectors $x_1, \ldots, x_p \in R^{m}$ and $A \in R^{d \times m}$ satisfying RIP such that $(A x_1, \ldots, A x_p)$ is "close" to $(y_1, \ldots, y_p)$. #R##N#Note that unlike known results in provable dictionary learning, there is no assumption on the distribution of $x_1, \dots, x_p$ in the YES case. The goal is to design an efficient algorithm that can distinguish the above two cases using linear queries, i.e., inner products of the input vectors with vectors of our choice. #R##N#Our tester is very simple and efficient: it outputs YES iff the gaussian width of the input is sufficiently small. The analysis of the test can be viewed as giving a robust characterization of gaussian width in terms of sparsity. The number of linear queries made by the tester is independent of the dimension.

Includes members:
  Member "dictionary learning and tensor decomposition via the sum of squares method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "distributed private heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the power of multiple anonymous messages")
  Member "exact recovery of sparsely used dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "on the geometry of differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privately learning markov random fields")

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)