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: "a parallel space saving algorithm for frequent items and the hurwitz zeta distribution"
  Abstract: We design a parallel algorithm for frequent items based on the sequential Space Saving algorithm.We show that the algorithm is cost-optimal for k = O ( 1 ) and therefore extremely fast and useful in a wide range of applications.We experimentally validate the algorithm on synthetic data distributed using a Hurwitz zeta function and a Zipf one, and also on real datasets, confirming the theoretical cost-optimality and that the error committed is very low and close to zero.We compare the performances and the error committed by our algorithm against a parallel algorithm recently proposed by Agarwal et?al. for merging datasets derived by the Space Saving or Frequent algorithms. We present a message-passing based parallel version of the Space Saving algorithm designed to solve the k-majority problem. The algorithm determines in parallel frequent items, i.e., those whose frequency is greater than a given threshold, and is therefore useful for iceberg queries and many other different contexts. We apply our algorithm to the detection of frequent items in both real and synthetic datasets whose probability distribution functions are a Hurwitz and a Zipf distribution respectively. Also, we compare its parallel performances and accuracy against a parallel algorithm recently proposed for merging summaries derived by the Space Saving or Frequent algorithms.

Connected through hyperedge HE-1 (induced by source paper "mining frequent items in unstructured p2p networks"):
  with members:
    - "parallel mining of time faded heavy hitters"
    - "parallel space saving on multi and many core processors"
    - "fast and accurate mining of correlated heavy hitters"
    - "mining frequent items in the time fading model"
    - "on the influence of graph density on randomized gossiping"
    - "identifying correlated heavy hitters in a two dimensional data stream"

Connected through hyperedge HE-2 (induced by source paper "parallel mining of time faded heavy hitters"):
  with members:
    - "fast and accurate mining of correlated heavy hitters"
    - "mining frequent items in the time fading model"
    - "identifying correlated heavy hitters in a two dimensional data stream"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate mining of correlated heavy hitters"):
  with members:
    - "parallel space saving on multi and many core processors"
    - "mining frequent items in the time fading model"
    - "identifying correlated heavy hitters in a two dimensional data stream"

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)