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 "tight bounds for the subspace sketch problem with applications"):
  Abstract: In the subspace sketch problem one is given an $n\times d$ matrix $A$ with $O(\log(nd))$ bit entries, and would like to compress it in an arbitrary way to build a small space data structure $Q_p$, so that for any given $x \in \mathbb{R}^d$, with probability at least $2/3$, one has $Q_p(x)=(1\pm\epsilon)\|Ax\|_p$, where $p\geq 0$, and where the randomness is over the construction of $Q_p$. The central question is: How many bits are necessary to store $Q_p$? #R##N#This problem has applications to the communication of approximating the number of non-zeros in a matrix product, the size of coresets in projective clustering, the memory of streaming algorithms for regression in the row-update model, and embedding subspaces of $L_p$ in functional analysis. A major open question is the dependence on the approximation factor $\epsilon$. #R##N#We show if $p\geq 0$ is not a positive even integer and $d=\Omega(\log(1/\epsilon))$, then $\tilde{\Omega}(\epsilon^{-2}d)$ bits are necessary. On the other hand, if $p$ is a positive even integer, then there is an upper bound of $O(d^p\log(nd))$ bits independent of $\epsilon$. Our results are optimal up to logarithmic factors, and show in particular that one cannot compress $A$ to $O(d)$ "directions" $v_1,\dots,v_{O(d)}$, such that for any $x$, $\|Ax\|_1$ can be well-approximated from $\langle v_1,x\rangle,\dots,\langle v_{O(d)},x\rangle$. Our lower bound rules out arbitrary functions of these inner products (and in fact arbitrary data structures built from $A$), and thus rules out the possibility of a singular value decomposition for $\ell_1$ in a very strong sense. Indeed, as $\epsilon\to 0$, for $p = 1$ the space complexity becomes arbitrarily large, while for $p = 2$ it is at most $O(d^2 \log(nd))$. As corollaries of our main lower bound, we obtain new lower bounds for a wide range of applications, including the above, which in many cases are optimal.

Includes members:
  Member "distributed statistical estimation of matrix products with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the communication complexity of optimization")
  Member "high probability frequency moment sketches":
    also connected through other hyperedges:
      (none)
  Member "optimal lower bounds for sketching graph cuts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions")
  Member "streaming space complexity of nearly all functions of one variable on frequency vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "on sketching quadratic forms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "on the communication complexity of linear algebraic problems in the message passing model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "space lower bounds for linear prediction")
  Member "quantile regression for large scale applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient symmetric norm regression via linear sketching")

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)