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 "oblivious resampling oracles and 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. A framework of Harvey & Vondr\'ak (2015) based on "resampling oracles" extended this give very general sequential algorithms for other probability spaces satisfying the Lopsided Lov\'asz Local Lemma (LLLL). #R##N#We describe a new structural property of resampling oracles, which we refer to as "obliviousness." Essentially, it means that the interaction between two bad-events $B, B'$ depends only on the randomness used to resample $B$, and not on the precise state within $B$ itself. #R##N#This property has two major benefits. First, it is the key to achieving a unified parallel LLLL algorithm, which is faster than previous, problem-specific algorithms of Harris (2016) for the variable-assignment LLLL algorithm and of Harris & Srinivasan (2014) for permutations. This gives the first RNC algorithms for perfect matchings and hamiltonian cycles of $K_n$. #R##N#Second, this property allows us to build LLLL probability spaces out of a relatively simple "atomic" set of events. It was intuitively clear that existing LLLL spaces were built in this way, but the obliviousness property formalizes this and gives a way of automatically turning a resampling oracle for atomic events into a resampling oracle for conjunctions of them. Using this framework, we get the first sequential resampling oracle for perfect matchings on the complete $s$-uniform hypergraph $K_n^{(s)}$, and the first commutative resampling oracle for hamiltonian cycles of $K_n$.

Includes members:
  Member "beyond the lovasz local lemma point to set correlations and their algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extensions of the algorithmic lovasz local lemma")
  Member "tight analysis of randomized greedy mis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "derandomized concentration bounds for polynomials and hypergraph maximal independent set")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed maximum matching verification in congest")
  Member "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "algorithmic and enumerative aspects of the moser tardos distribution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "commutativity in the algorithmic lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extensions of the algorithmic lovasz local lemma")
  Member "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast sampling and counting k sat solutions in the local lemma regime")
  Member "an improved distributed algorithm for maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")

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)