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: "tight local approximation results for max min linear programs"
  Abstract: In a bipartite max-min LP, we are given a bipartite graph$\mathcal{G} = (V \cup I \cup K, E)$, where each agentv ∈ V is adjacent to exactly one constrainti ∈ I and exactly one objectivek ∈ K. Each agent v controls a variable x v .For each i ∈ I we have a nonnegative linearconstraint on the variables of adjacent agents. For eachk ∈ K we have a nonnegative linear objectivefunction of the variables of adjacent agents. The task is tomaximise the minimum of the objective functions. We study localalgorithms where each agent v must choose x v based on input withinits constant-radius neighbourhood in . We show that for everye> 0 there exists a local algorithm achieving theapproximation ratio Δ I (1 − 1/Δ K) + e. We also show that this result is thebest possible – no local algorithm can achieve theapproximation ratio Δ I (1 − 1/Δ K). Here Δ I is the maximum degree of a vertexi ∈ I, and Δ K is the maximum degree of avertex k ∈ K. As a methodological contribution,we introduce the technique of graph unfolding for the design oflocal approximation algorithms.

Connected through hyperedge HE-1 (induced by source paper "weak models of distributed computing with connections to modal logic"):
  with members:
    - "lower bounds for local approximation"
    - "beeping a maximal independent set"
    - "stone age distributed computing"
    - "almost stable matchings by truncating the gale shapley algorithm"
    - "local algorithms in weakly coloured graphs"
    - "deploying wireless networks with beeps"
    - "an optimal local approximation algorithm for max min linear programs"
    - "a simple local 3 approximation algorithm for vertex cover"

Connected through hyperedge HE-2 (induced by source paper "an optimal local approximation algorithm for max min linear programs"):
  with members:
    - "local approximation algorithms for a class of 0 1 max min linear programs"
    - "approximating max min linear programs with local algorithms"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-3 (induced by source paper "a simple local 3 approximation algorithm for vertex cover"):
  with members:
    - "approximating max min linear programs with local algorithms"

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.DC(Distributed, Parallel, and Cluster Computing)