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: "parallel algorithms for the lopsided lovasz local lemma"
  Abstract: The Lov\'asz Local Lemma (LLL) is a probabilistic tool which shows that, if a collection of "bad" events $\mathcal B$ in a probability space are not too likely and not too interdependent, then there is a positive probability that no bad-events in $\mathcal B$ occur. Moser & Tardos (2010) gave sequential and parallel algorithms which transformed most applications of the variable-assignment LLL into efficient algorithms. #R##N#There has been limited success in developing parallel algorithms for more generalized forms of the LLL. Harris (2016) developed RNC algorithms for the variable-assignment Lopsided Lov\'asz Local Lemma (LLLL) and Harris & Srinivasan (2014) developed an algorithm for the permutation LLL. These algorithms are cumbersome and limited in the types of bad-events they can handle (limitations not shared for the standard LLL). Kolmogorov (2016) developed a framework which partially parallelizes LLL settings such as random matchings of $K_n$, although key algorithm components remained missing. #R##N#We give new parallel algorithms for most forms of LLLL, which are simpler, faster, and more general than the algorithms of Harris and Harris & Srinivasan. This also includes probability spaces for which no previous RNC algorithm was known, including matchings and hamiltonian cycles of $K_n$. We achieve this by providing a unified algebraic framework for applications of the LLLL to permutations. #R##N#The parallel LLLL algorithm is based on new primitive for parallel computing, which we refer to as the \emph{lexicographically-first maximal-independent-set} of a directed graph. We give an efficient algorithm for constructing this and show that it is precisely what is needed for our LLLL algorithms. This generalizes an algorithm given by Blelloch, Fineman, Shun (2012) for undirected graphs.

Connected through hyperedge HE-1 (induced by source paper "on derandomizing local distributed algorithms"):
  with members:
    - "the complexity of distributed edge coloring with small palettes"
    - "deterministic distributed edge coloring with fewer colors"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "a time hierarchy theorem for the local model"
    - "distributed degree splitting edge coloring and orientations"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "on the complexity of local distributed graph problems"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"

Connected through hyperedge HE-2 (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"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "deterministic parallel algorithms for fooling polylogarithmic juntas and the 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)