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: "mining frequent items in the time fading model"
  Abstract: We introduce FDCMSS, a novel sketch-based algorithm for frequent items working in the time fading model. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms.We formally prove the correctness of our algorithm.We experimentally validate the algorithm on synthetic data distributed using a Zipf distribution, and also on real datasets.We compare the performances and the error committed by our algorithm against λ-HCount, an algorithm recently proposed by Chen and Mei. Extensive experimental results show that FDCMSS outperforms λ-HCount with regard to speed, space used, precision attained and error committed on both synthetic and real datasets. We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms λ-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

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"
    - "a parallel space saving algorithm for frequent items and the hurwitz zeta distribution"
    - "parallel space saving on multi and many core processors"
    - "fast and accurate mining of correlated heavy hitters"
    - "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:
    - "a parallel space saving algorithm for frequent items and the hurwitz zeta distribution"
    - "fast and accurate mining of correlated heavy hitters"
    - "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:
    - "a parallel space saving algorithm for frequent items and the hurwitz zeta distribution"
    - "parallel space saving on multi and many core processors"
    - "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)