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: "an approximation ratio for biclustering"
  Abstract: The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on the rows and on the columns of the input matrix. We show that such a solution yields a worst-case approximation ratio of 1+2 under L"1-norm for 0-1 valued matrices, and of 2 under L"2-norm for real valued matrices.

Connected through hyperedge HE-1 (induced by source paper "comparing apples and oranges measuring differences between data mining results"):
  with members:
    - "distances between data sets based on summary statistics"
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-2 (induced by source paper "comparing apples and oranges measuring differences between exploratory data mining results"):
  with members:
    - "comparing apples and oranges measuring differences between data mining results"
    - "distances between data sets based on summary statistics"
    - "summarizing data succinctly with the most informative itemsets"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "computational complexity of queries based on 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)