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: "mining top k frequent itemsets through progressive sampling"
  Abstract: We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very frequent (resp., very infrequent) itemsets, together with an estimate of these itemsets' frequencies with a bounded error. Our first result is an upper bound on the sample size which guarantees that the top-K frequent itemsets mined from a random sample of that size approximate the actual top-K frequent itemsets, with probability larger than a specified value. We show that the upper bound is asymptotically tight when w is constant. Our main algorithmic contribution is a progressive sampling approach, combined with suitable stopping conditions, which on appropriate inputs is able to extract approximate top-K frequent itemsets from samples whose sizes are smaller than the general upper bound. In order to test the stopping conditions, this approach maintains the frequency of all itemsets encountered, which is practical only for small w. However, we show how this problem can be mitigated by using a variation of Bloom filters. A number of experiments conducted on both synthetic and real benchmark datasets show that using samples substantially smaller than the original dataset (i.e., of size defined by the upper bound or reached through the progressive sampling approach) enable to approximate the actual top-K frequent itemsets with accuracy much higher than what analytically proved.

Connected through hyperedge HE-1 (induced by source paper "a new frequency estimation sketch for data streams"):
  with members:
    - "a shifting bloom filter framework for set queries"

Connected through hyperedge HE-2 (induced by source paper "adaptive sampling for rapidly matching histograms"):
  with members:
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "visualization aware sampling for very large databases"
    - "l p testing and learning of discrete distributions"
    - "rapid sampling for visualizations with ordering guarantees"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "pf ola a high performance framework for parallel on line aggregation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "tracking influential individuals in dynamic networks"):
  with members:
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-4 (induced by source paper "an asymptotically tighter bound on sampling for frequent itemsets mining"):
  with members:
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "finding the true frequent itemsets"
    - "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"

Connected through hyperedge HE-5 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "metropolis hastings algorithms for estimating betweenness centrality in large networks"
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "bubing massive crawling for the masses"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"

Connected through hyperedge HE-6 (induced by source paper "clustering uncertain graphs"):
  with members:
    - "maximizing social influence in nearly optimal time"
    - "injecting uncertainty in graphs for identity obfuscation"

Connected through hyperedge HE-7 (induced by source paper "sanitization of call detail records via differentially private summaries"):
  with members:
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "the vc dimension of queries and selectivity estimation through sampling"
    - "shape fitting on point sets with probability distributions"

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)