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 tracking of distributed heavy hitters and quantiles"
  Abstract: We consider the the problem of tracking heavy hitters and quantiles in the distributed streaming model. The heavy hitters and quantiles are two important statistics for characterizing a data distribution. Let $A$ be a multiset of elements, drawn from the universe $U=\{1,...,u\}$. For a given $0 \le \phi \le 1$, the $\phi$-heavy hitters are those elements of $A$ whose frequency in $A$ is at least $\phi |A|$; the $\phi$-quantile of $A$ is an element $x$ of $U$ such that at most $\phi|A|$ elements of $A$ are smaller than $A$ and at most $(1-\phi)|A|$ elements of $A$ are greater than $x$. Suppose the elements of $A$ are received at $k$ remote {\em sites} over time, and each of the sites has a two-way communication channel to a designated {\em coordinator}, whose goal is to track the set of $\phi$-heavy hitters and the $\phi$-quantile of $A$ approximately at all times with minimum communication. We give tracking algorithms with worst-case communication cost $O(k/\eps \cdot \log n)$ for both problems, where $n$ is the total number of items in $A$, and $\eps$ is the approximation error. This substantially improves upon the previous known algorithms. We also give matching lower bounds on the communication costs for both problems, showing that our algorithms are optimal. We also consider a more general version of the problem where we simultaneously track the $\phi$-quantiles for all $0 \le \phi \le 1$.

Connected through hyperedge HE-1 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "streaming quantiles algorithms with small space and update time"):
  with members:
    - "optimal quantile approximation in streams"
    - "xgboost a scalable tree boosting system"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-5 (induced by source paper "a survey of challenges for runtime verification from advanced application domains beyond software"):
  with members:
    - "adventures in monitorability from branching to linear time and back again"
    - "after you please browser extensions order attacks and countermeasures"
    - "selective monitoring"
    - "monitoring data minimisation"
    - "on the monitoring of decentralized specifications semantics properties analysis and simulation"
    - "an algebraic framework for runtime verification"
    - "learning to protect communications with adversarial neural cryptography"
    - "a web based tool for analysing normative documents in english"

Connected through hyperedge HE-6 (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-7 (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"
    - "randomized algorithms for tracking distributed count frequencies and ranks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "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)