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 "on sketching quadratic forms"):
  Abstract: We undertake a systematic study of sketching a quadratic form: given an $n \times n$ matrix $A$, create a succinct sketch $\textbf{sk}(A)$ which can produce (without further access to $A$) a multiplicative $(1+\epsilon)$-approximation to $x^T A x$ for any desired query $x \in \mathbb{R}^n$. While a general matrix does not admit non-trivial sketches, positive semi-definite (PSD) matrices admit sketches of size $\Theta(\epsilon^{-2} n)$, via the Johnson-Lindenstrauss lemma, achieving the "for each" guarantee, namely, for each query $x$, with a constant probability the sketch succeeds. (For the stronger "for all" guarantee, where the sketch succeeds for all $x$'s simultaneously, again there are no non-trivial sketches.) #R##N#We design significantly better sketches for the important subclass of graph Laplacian matrices, which we also extend to symmetric diagonally dominant matrices. A sequence of work culminating in that of Batson, Spielman, and Srivastava (SIAM Review, 2014), shows that by choosing and reweighting $O(\epsilon^{-2} n)$ edges in a graph, one achieves the "for all" guarantee. Our main results advance this front. #R##N#$\bullet$ For the "for all" guarantee, we prove that Batson et al.'s bound is optimal even when we restrict to "cut queries" $x\in \{0,1\}^n$. #R##N#In contrast, previous lower bounds showed the bound only for {\em spectral-sparsifiers}. #R##N#$\bullet$ For the "for each" guarantee, we design a sketch of size $\tilde O(\epsilon^{-1} n)$ bits for "cut queries" $x\in \{0,1\}^n$. We prove a nearly-matching lower bound of $\Omega(\epsilon^{-1} n)$ bits. For general queries $x \in \mathbb{R}^n$, we construct sketches of size $\tilde{O}(\epsilon^{-1.6} n)$ bits.

Includes members:
  Member "circulant matrices and differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "single pass spectral sparsification in dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "distributed computation of large scale graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "when distributed computation is communication expensive":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the communication complexity of optimization")
  Member "differentially private data analysis of social networks via restricted sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private identity testing for high dimensional distributions")
  Member "iterative constructions and private data release":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")
  Member "fast approximation algorithms for cut based problems in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "twice ramanujan sparsifiers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketching transformed matrices with applications to natural language processing")

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)