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: "computational complexity of queries based on itemsets"
  Abstract: We investigate determining the exact bounds of the frequencies of conjunctions based on frequent sets. Our scenario is an important special case of some general probabilistic logic problems that are known to be intractable. We show that despite the limitations our problems are also intractable, namely, we show that checking whether the maximal consistent frequency of a query is larger than a given threshold is NP-complete and that evaluating the Maximum Entropy estimate of a query is PP-hard. We also prove that checking consistency is NP-complete.

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"
    - "an approximation ratio for biclustering"

Connected through hyperedge HE-2 (induced by source paper "interactive and iterative discovery of entity network subgraphs"):
  with members:
    - "examine exploring annotated modules in networks"
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "summarizing data succinctly with the most informative itemsets"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "interesting multi relational patterns"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-3 (induced by source paper "generating realistic synthetic population datasets"):
  with members:
    - "comparing apples and oranges measuring differences between 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"
    - "finding good itemsets by packing data"

Connected through hyperedge HE-4 (induced by source paper "interactive discovery of coordinated relationship chains with maximum entropy models"):
  with members:
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "summarizing data succinctly with the most informative itemsets"
    - "interesting multi relational patterns"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "introduction to machine learning class notes 67577"

Connected through hyperedge HE-5 (induced by source paper "association rule mining using maximum entropy"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"

Connected through hyperedge HE-6 (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"
    - "an approximation ratio for biclustering"

Connected through hyperedge HE-7 (induced by source paper "summarizing data succinctly with the most informative itemsets"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "using background knowledge to rank itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "probably the best itemsets"
    - "maximum entropy based significance of itemsets"

Connected through hyperedge HE-8 (induced by source paper "tell me what i need to know succinctly summarizing data with itemsets"):
  with members:
    - "using background knowledge to rank itemsets"
    - "probably the best itemsets"
    - "maximum entropy based significance of 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.CC(Computational Complexity)