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: "safe projections of binary data sets"
  Abstract: Selectivity estimation of a boolean query based on frequent itemsets can be solved by describing the problem by a linear program. However, the number of variables in the equations is exponential, rendering the approach tractable only for small-dimensional cases. One natural approach would be to project the data to the variables occurring in the query. This can, however, change the outcome of the linear program.#R##N##R##N#We introduce the concept of safe sets: projecting the data to a safe set does not change the outcome of the linear program. We characterise safe sets using graph theoretic concepts and give an algorithm for finding minimal safe sets containing given attributes. We describe a heuristic algorithm for finding almost-safe sets given a size restriction, and show empirically that these sets outperform the trivial projection.#R##N##R##N#We also show a connection between safe sets and Markov Random Fields and use it to furtherreduce the number of variables in the linear program, given some regularity assumptions on the frequent itemsets.

Connected through hyperedge HE-1 (induced by source paper "using background knowledge to rank itemsets"):
  with members:
    - "maximum entropy based significance of itemsets"

Connected through hyperedge HE-2 (induced by source paper "maximum entropy based significance of itemsets"):
  with members:
    - "computational complexity of queries based on itemsets"
    - "mining all non derivable frequent itemsets"

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)