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: "all distances sketches revisited hip estimators for massive graphs analysis"
  Abstract: Graph datasets with billions of edges, such as social and Web graphs, are prevalent, and scalable computation is critical. All-distances sketches (ADS) [Cohen 1997], are a powerful tool for scalable approximation of statistics. #R##N#The sketch is a small size sample of the distance relation of a node which emphasizes closer nodes. Sketches for all nodes are computed using a nearly linear computation and estimators are applied to sketches of nodes to estimate their properties. #R##N#We provide, for the first time, a unified exposition of ADS algorithms and applications. We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of statistics. For the important special cases of neighborhood cardinalities (the number of nodes within some query distance) and closeness centralities, HIP estimators have at most half the variance of previous estimators and we show that this is essentially optimal. Moreover, HIP obtains a polynomial improvement for more general statistics and the estimators are simple, flexible, unbiased, and elegant. #R##N#For approximate distinct counting on data streams, HIP outperforms the original estimators for the HyperLogLog MinHash sketches (Flajolet et al. 2007), obtaining significantly improved estimation quality for this state-of-the-art practical algorithm.

Connected through hyperedge HE-1 (induced by source paper "sampling sketches for concave sublinear functions of frequencies"):
  with members:
    - "universal streaming of subset norms"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "distributed representations of words and phrases and their compositionality"
    - "streaming algorithms from precision sampling"
    - "growing a tree in the forest constructing folksonomies by integrating structured metadata"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-2 (induced by source paper "sample complexity bounds for influence maximization"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "multi objective weighted sampling"
    - "the limitations of optimization from samples"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-3 (induced by source paper "fast concurrent data sketches"):
  with members:
    - "distributionally linearizable data structures"
    - "sketching linear classifiers over data streams"
    - "multiqueues simpler faster and better relaxed concurrent priority queues"
    - "linearizable implementations do not suffice for randomized distributed computation"

Connected through hyperedge HE-4 (induced by source paper "how to maximize the spread of social influence a survey"):
  with members:
    - "influence maximization with bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

Connected through hyperedge HE-5 (induced by source paper "near optimal clustering in the k machine model"):
  with members:
    - "tight bounds for distributed graph computations"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "approximate undirected transshipment and shortest paths via gradient descent"
    - "scalable facility location for massive graphs on pregel like systems"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "distributed computation of large scale graph problems"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-6 (induced by source paper "new cardinality estimation methods for hyperloglog sketches"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "a framework for estimating stream expression cardinalities"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-7 (induced by source paper "new cardinality estimation algorithms for hyperloglog sketches"):
  with members:
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "a framework for estimating stream expression cardinalities"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-8 (induced by source paper "back to the future an even more nearly optimal cardinality estimation algorithm"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "a framework for estimating stream expression cardinalities"

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)