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 "querying a matrix through matrix vector products"):
  Abstract: We consider algorithms with access to an unknown matrix $M\in\mathbb{F}^{n \times d}$ via matrix-vector products, namely, the algorithm chooses vectors $\mathbf{v}^1, \ldots, \mathbf{v}^q$, and observes $M\mathbf{v}^1,\ldots, M\mathbf{v}^q$. Here the $\mathbf{v}^i$ can be randomized as well as chosen adaptively as a function of $ M\mathbf{v}^1,\ldots,M\mathbf{v}^{i-1}$. Motivated by applications of sketching in distributed computation, linear algebra, and streaming models, as well as connections to areas such as communication complexity and property testing, we initiate the study of the number $q$ of queries needed to solve various fundamental problems. We study problems in three broad categories, including linear algebra, statistics problems, and graph problems. For example, we consider the number of queries required to approximate the rank, trace, maximum eigenvalue, and norms of a matrix $M$; to compute the AND/OR/Parity of each column or row of $M$, to decide whether there are identical columns or rows in $M$ or whether $M$ is symmetric, diagonal, or unitary; or to compute whether a graph defined by $M$ is connected or triangle-free. We also show separations for algorithms that are allowed to obtain matrix-vector products only by querying vectors on the right, versus algorithms that can query vectors on both the left and the right. We also show separations depending on the underlying field the matrix-vector product occurs in. For graph problems, we show separations depending on the form of the matrix (bipartite adjacency versus signed edge-vertex incidence matrix) to represent the graph. #R##N#Surprisingly, this fundamental model does not appear to have been studied on its own, and we believe a thorough investigation of problems in this model would be beneficial to a number of different application areas.

Includes members:
  Member "tight query complexity lower bounds for pca via finite sample deformed wigner law":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the randomized complexity of minimizing a convex quadratic function")
  Member "improved algorithms for adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "testing matrix rank optimally":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "space lower bounds for linear prediction")
  Member "near optimal linear decision trees for k sum and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "all non trivial variants of 3 ldt are equivalent")
  Member "on estimating maximum matching size in graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "embeddings of schatten norms with applications to data streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "on approximating functions of the singular values in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "matrix norms in data streams faster multi pass and row order":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")

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.CC(Computational Complexity)