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 "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma"):
  Abstract: Many randomized algorithms can be derandomized efficiently using either the method of conditional expectations or probability spaces with low (almost-) independence. A series of papers, beginning with work by Luby (1988) and continuing with Berger & Rompel (1991) and Chari et al. (1994), showed that these techniques can be combined to give deterministic parallel algorithms for combinatorial optimization problems involving sums of $w$-juntas. We improve these algorithms through derandomized variable partitioning. This reduces the processor complexity to essentially independent of $w$ while the running time is reduced from exponential in $w$ to approximately $O(w)$. For example, we improve the time complexity of an algorithm of Berger & Rompel (1991) for rainbow hypergraph coloring by a factor of approximately $\log^2 n$ and the processor complexity by a factor of approximately $m^{\ln 2}$. #R##N#As a major application of this, we give an NC algorithm for the Lov\'{a}sz Local Lemma. Previous NC algorithms, including Moser & Tardos (2010) and Chandrasekaran et. al (2013), required that (essentially) the bad-events could span only $O(\log n)$ variables; we relax this to allowing $\text{polylog}(n)$ variables. As two applications of our new algorithm, we give algorithms for defective vertex coloring and domatic graph partition. #R##N#One main sub-problem encountered in these algorithms is to generate a probability space which can "fool" a given list of $GF(2)$ Fourier characters. Schulman (1992) gave an NC algorithm for this; we dramatically improve its efficiency to near-optimal time and processor complexity and code dimension. This leads to a new algorithm to solve the heavy-codeword problem, introduced by Naor & Naor (1993), with a near-linear processor compliexty $(mn)^{1+o(1)}$; this improves on the algorithm of Chari et. al. (1994) requiring $O(m n^2)$ processors.

Includes members:
  Member "deterministic parallel algorithms for bilinear objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space")
  Member "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classification of distributed binary labeling problems")
  Member "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast sampling and counting k sat solutions in the local lemma regime")
  Member "deterministic algorithms for the lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counting solutions to random cnf formulas")
  Member "a constructive proof of the general lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scattering and sparse partitions and their applications")

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)