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: "on randomized memoryless algorithms for the weighted k server problem"
  Abstract: The weighted k-server problem is a generalization of the k-server problem in which the cost of moving a server of weight βi through a distance d is βi · d. The weighted server problem on uniform spaces models caching where caches have different write costs. We prove tight bounds on the performance of randomized memoryless algorithms for this problem on uniform metric spaces. We prove that there is an αk competitive memoryless algorithm for this problem, where αk = α2k-1 + 3αk-1 + 1; α1 = 1. On the other hand, we also prove a lower bound result, which is a strong evidence to our conjecture, that no randomized memoryless algorithm can have competitive ratio better than αk. To prove the upper bound of αk, we develop a framework to bound from above the competitive ratio of any randomized memoryless algorithm for this problem. The key technical contribution is a method for working with potential functions defined implicitly as the solution of a linear system. The result is robust in the sense that a small change in the probabilities used by the algorithm results in a small change in the upper bound on the competitive ratio. The above result has two important implications. Firstly this yields an αk-competitive memoryless algorithm for the weighted k-server problem on uniform spaces. This is the first competitive algorithm for k > 2 which is memoryless. For k = 2, our algorithm agrees with the one given by Chrobak and Sgall [1]. Secondly, this helps us prove that the Harmonic algorithm, which chooses probabilities in inverse proportion to weights, has a competitive ratio of kαk. The only known competitive algorithm for every k before this work is a carefully crafted deterministic algorithm due to Fiat and Ricklin [2]. Their algorithm uses memory crucially and their bound on competitive ratio more than 24k . Our algorithm is not only memoryless, but also has a considerably improved competitive ratio of αk <; 1.62k. Further, the derandomization technique by Ben-David et al. [3] implies that there exists a deterministic algorithm for this problem with competitive ratio α2k <; 2.562k.

Connected through hyperedge HE-1 (induced by source paper "slaying hydrae improved bounds for generalized k server in uniform metrics"):
  with members:
    - "weighted k server bounds via combinatorial dichotomies"
    - "fusible hsts and the randomized k server conjecture"
    - "competitive algorithms for generalized k server in uniform metrics"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-2 (induced by source paper "weighted k server bounds via combinatorial dichotomies"):
  with members:
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "on the continuous cnn problem"
    - "the k server dual and loose competitiveness for paging"

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)