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: "improved algorithms for distributed entropy monitoring"
  Abstract: Modern data management systems often need to deal with massive, dynamic and inherently distributed data sources. We collect the data using a distributed network, and at the same time try to maintain a global view of the data at a central coordinator using a minimal amount of communication. Such applications have been captured by the distributed monitoring model which has attracted a lot of attention in recent years. In this paper we investigate the monitoring of the entropy functions, which are very useful in network monitoring applications such as detecting distributed denial-of-service attacks. Our results improve the previous best results by Arackaparambil et al. [2]. Our technical contribution also includes implementing the celebrated AMS sampling method (by Alon et al. [1]) in the distributed monitoring model, which could be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "weighted reservoir sampling from distributed streams"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted sampling without replacement from data streams"
    - "when distributed computation is communication expensive"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "streaming algorithms from precision sampling"
    - "optimal tracking of distributed heavy hitters and quantiles"

Connected through hyperedge HE-2 (induced by source paper "streaming communication protocols"):
  with members:
    - "maximum matching in turnstile streams"
    - "better bounds for matchings in the streaming model"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for distributed functional monitoring"
    - "recognizing well parenthesized expressions in the streaming model"
    - "continuous monitoring of distributed data streams over a time based sliding window"
    - "optimal tracking of distributed heavy hitters and quantiles"
    - "on the complexity of processing massive unordered distributed data"

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)