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: "optimal random sampling from distributed streams revisited"
  Abstract: We give an improved algorithm for drawing a random sample from a large data stream when the input elements are distributed across multiple sites which communicate via a central coordinator. At any point in time the set of elements held by the coordinator represent a uniform random sample from the set of all the elements observed so far. When compared with prior work, our algorithms asymptotically improve the total number of messages sent in the system as well as the computation required of the coordinator. We also present a matching lower bound, showing that our protocol sends the optimal number of messages up to a constant factor with large probability. As a byproduct, we obtain an improved algorithm for finding the heavy hitters across multiple distributed sites.

Connected through hyperedge HE-1 (induced by source paper "communication efficient weighted reservoir sampling from fully distributed data streams"):
  with members:
    - "weighted reservoir sampling from distributed streams"
    - "parallel weighted random sampling"
    - "parallel streaming random sampling"
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "weighted sampling without replacement from data streams"
    - "fast parallel operations on search trees"

Connected through hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"

Connected through hyperedge HE-3 (induced by source paper "weighted reservoir sampling from distributed streams"):
  with members:
    - "weighted sampling without replacement from data streams"
    - "improved algorithms for distributed entropy monitoring"
    - "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-4 (induced by source paper "communication efficient weighted reservoir sampling"):
  with members:
    - "weighted reservoir sampling from distributed streams"
    - "parallel weighted random sampling"
    - "weighted sampling without replacement from data streams"
    - "fast parallel operations on search trees"
    - "practical massively parallel sorting"
    - "weighted random sampling over data streams"

Connected through hyperedge HE-5 (induced by source paper "distributed monitoring of election winners"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "proportional representation in vote streams"
    - "optimizing positional scoring rules for rank aggregation"
    - "fishing out winners from vote streams"
    - "estimating the margin of victory of an election using sampling"
    - "sample complexity for winner prediction in elections"
    - "crowdsourcing for participatory democracies efficient elicitation of social choice functions"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-6 (induced by source paper "perfect l_p sampling in a data stream"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "heavy hitters via cluster preserving clustering"
    - "pseudorandomness via the discrete fourier transform"
    - "beating countsketch for heavy hitters in insertion streams"
    - "stream sampling for frequency cap statistics"
    - "improved concentration bounds for count sketch"
    - "structure aware sampling flexible and accurate summarization"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-7 (induced by source paper "distinct sampling on streaming data with near duplicates"):
  with members:
    - "maximum matching in turnstile streams"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-8 (induced by source paper "improved algorithms for distributed entropy monitoring"):
  with members:
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for distributed functional monitoring"
    - "continuous monitoring of distributed data streams over a time based sliding window"
    - "optimal tracking of distributed heavy hitters and quantiles"
    - "sketching and streaming entropy via approximation theory"
    - "streaming and sublinear approximation of entropy and information distances"

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.DC(Distributed, Parallel, and Cluster Computing)