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 scheduler is very powerful in competitive analysis of distributed list accessing"
  Abstract: This work is a continuation of efforts to define and understand competitive analysis of algorithms in a distributed shared memory setting, which is surprisingly different from the classical online setting. In fact, in a distributed shared memory setting, we find a counter-example to the theorem concerning classical randomized online algorithms which shows that, if there is a $c$-competitive randomized algorithm against an adaptive offline adversary, then there is a $c$-competitive deterministic algorithm [Ben-David, Borodin, Karp, Tardos, Wigderson, 1994]. In a distributed setting, there is additional lack of knowledge concerning what the other processes have done. There is also additional power for the adversary, having control of the scheduler which decides when each process is allowed to take steps. #R##N#We consider the list accessing problem, which is a benchmark problem for sequential online algorithms. In the distributed version of this problem, each process has its own finite sequence of requests to a shared list. The scheduler arises as a major issue in its competitive analysis. We introduce two different adversaries, which differ in how they are allowed to schedule processes, and use them to perform competitive analysis of distributed list accessing. We prove tight upper and lower bounds on combinatorial properties of merges of the request sequences, which we use in the analysis. Our analysis shows that the effects of the adversarial scheduler can be quite significant, dominating the usual quality loss due to lack of information about the future.

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)