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: "space lower bounds for itemset frequency sketches"
  Abstract: Given a database, computing the fraction of rows that contain a query itemset or determining whether this fraction is above some threshold are fundamental operations in data mining. A uniform sample of rows is a good sketch of the database in the sense that all sufficiently frequent itemsets and their approximate frequencies are recoverable from the sample, and the sketch size is independent of the number of rows in the original database. For many seemingly similar problems there are better sketching algorithms than uniform sampling. In this paper we show that for itemset frequency sketching this is not the case. That is, we prove that there exist classes of databases for which uniform sampling is a space optimal sketch for approximate itemset frequency analysis, up to constant or iterated-logarithmic factors.

Connected through hyperedge HE-1 (induced by source paper "finding subcube heavy hitters in analytics data streams"):
  with members:
    - "testing ising models"
    - "graphical model sketch"
    - "measuring independence of datasets"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-2 (induced by source paper "finding subcube heavy hitters in data streams"):
  with members:
    - "graphical model sketch"
    - "measuring independence of datasets"

Connected through hyperedge HE-3 (induced by source paper "fingerprinting codes and the price of approximate differential privacy"):
  with members:
    - "preventing false discovery in interactive data analysis is hard"
    - "characterizing the sample complexity of private learners"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "faster private release of marginals on small databases"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"

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)