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: "randomized algorithms for tracking distributed count frequencies and ranks"
  Abstract: We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the {\em count-tracking} problem, where there are $k$ players, each holding a counter $n_i$ that gets incremented over time, and the goal is to track an $\eps$-approximation of their sum $n=\sum_i n_i$ continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is $\Theta(k/\eps \cdot \log N)$, where $N$ is the final value of $n$ when the tracking finishes, we show that with randomization, the communication cost can be reduced to $\Theta(\sqrt{k}/\eps \cdot \log N)$. Our algorithm is simple and uses only O(1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems: {\em frequency-tracking} and {\em rank-tracking}, and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature.

Connected through hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "distributed monitoring of election winners"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "optimal random sampling from distributed streams revisited"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning graphical models from a distributed stream"):
  with members:
    - "communication optimal distributed clustering"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "graphical model sketch"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "distributed learning communication complexity and privacy"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Connected through hyperedge HE-5 (induced by source paper "a communication efficient distributed data structure for top k and k select queries"):
  with members:
    - "monitoring of domain related problems in distributed data streams"
    - "on competitive algorithms for approximations of top k position monitoring of distributed streams"
    - "online top k position monitoring of distributed data streams"
    - "optimal tracking of distributed heavy hitters and quantiles"

Connected through hyperedge HE-6 (induced by source paper "monitoring of domain related problems in distributed data streams"):
  with members:
    - "on competitive algorithms for approximations of top k position monitoring of distributed streams"
    - "online top k position monitoring of distributed data streams"
    - "tight bounds for distributed functional monitoring"

Connected through hyperedge HE-7 (induced by source paper "streaming communication protocols"):
  with members:
    - "maximum matching in turnstile streams"
    - "improved algorithms for distributed entropy monitoring"
    - "better bounds for matchings in the streaming model"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "variability in data streams"):
  with members:
    - "tight bounds for distributed functional monitoring"
    - "optimal tracking of distributed heavy hitters and quantiles"
    - "cr precis a deterministic summary structure for update data streams"

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)