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: "the power of choice in priority scheduling"
  Abstract: Consider the following random process: we are given $n$ queues, into which elements of increasing labels are inserted uniformly at random. To remove an element, we pick two queues at random, and remove the element of lower label (higher priority) among the two. The cost of a removal is the rank of the label removed, among labels still present in any of the queues, that is, the distance from the optimal choice at each step. Variants of this strategy are prevalent in state-of-the-art concurrent priority queue implementations. Nonetheless, it is not known whether such implementations provide any rank guarantees, even in a sequential model. #R##N#We answer this question, showing that this strategy provides surprisingly strong guarantees: Although the single-choice process, where we always insert and remove from a single randomly chosen queue, has degrading cost, going to infinity as we increase the number of steps, in the two choice process, the expected rank of a removed element is $O( n )$ while the expected worst-case cost is $O( n \log n )$. These bounds are tight, and hold irrespective of the number of steps for which we run the process. #R##N#The argument is based on a new technical connection between "heavily loaded" balls-into-bins processes and priority scheduling. #R##N#Our analytic results inspire a new concurrent priority queue implementation, which improves upon the state of the art in terms of practical performance.

Connected through hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the lock free k lsm relaxed priority queue"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-2 (induced by source paper "monotonically relaxing concurrent data structure semantics for performance an efficient 2d design framework"):
  with members:
    - "the lock free k lsm relaxed priority queue"
    - "a dynamic elimination combining stack algorithm"
    - "flat combining and the synchronization parallelism tradeoff"

Connected through hyperedge HE-3 (induced by source paper "relaxed schedulers can efficiently parallelize iterative algorithms"):
  with members:
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-4 (induced by source paper "distributionally linearizable data structures"):
  with members:
    - "tight bounds for parallel randomized load balancing"

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)