human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel"):
  with members:
    - "algorithms for weighted independent transversals and strong colouring"
    - "deterministic parallel algorithms for bilinear objective functions"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "algorithmic and enumerative aspects of the moser tardos distribution"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "new constructive aspects of the lovasz local lemma"
    - "deterministic algorithms for the lovasz local lemma"

Connected through hyperedge HE-2 (induced by source paper "derandomizing the lovasz local lemma via log space statistical tests"):
  with members:
    - "deterministic parallel algorithms for bilinear objective functions"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"

Connected through hyperedge HE-3 (induced by source paper "variable version lov asz local lemma beyond shearer s bound"):
  with members:
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "uniform sampling through the lov asz local lemma"
    - "computing the independence polynomial in shearer s region for the lll"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "focused stochastic local search and the lov asz local lemma"
    - "commutativity in the algorithmic lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"

Connected through hyperedge HE-4 (induced by source paper "deterministic parallel algorithms for bilinear objective functions"):
  with members:
    - "a derandomized sparse johnson lindenstrauss transform"

Connected through hyperedge HE-5 (induced by source paper "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"):
  with members:
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "parallel algorithms for the lopsided lovasz local lemma"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "an improved distributed algorithm for maximal independent set"
    - "new constructive aspects of the lovasz local lemma"
    - "deterministic algorithms for the lovasz local lemma"
    - "a constructive proof of the general lovasz local lemma"

Question: Please tell me which class the center node 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)