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: "tell me what i need to know succinctly summarizing data with itemsets"
  Abstract: Data analysis is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and hence, what result we would find the most interesting. With this in mind, we introduce a well-founded approach for succinctly summarizing data with a collection of itemsets; using a probabilistic maximum entropy model, we iteratively find the most interesting itemset, and in turn update our model of the data accordingly. As we only include itemsets that are surprising with regard to the current model, the summary is guaranteed to be both descriptive and non-redundant. The algorithm that we present can either mine the top-k most interesting itemsets, or use the Bayesian Information Criterion to automatically identify the model containing only the itemsets most important for describing the data. Or, in other words, it will 'tell you what you need to know'. Experiments on synthetic and benchmark data show that the discovered summaries are succinct, and correctly identify the key patterns in the data. The models they form attain high likelihoods, and inspection shows that they summarize the data well with increasingly specific, yet non-redundant itemsets.

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"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "an approximation ratio for biclustering"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-2 (induced by source paper "summarizing event sequences with serial episodes a statistical model and an application"):
  with members:
    - "discovering compressing serial episodes from event sequences"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-3 (induced by source paper "discovering descriptive tile trees by mining optimal geometric subtiles"):
  with members:
    - "are your items in order"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"

Connected through hyperedge HE-4 (induced by source paper "quantifying the informativeness for biomedical literature summarization an itemset mining method"):
  with members:
    - "summarization from medical documents a survey"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "nltk the natural language toolkit"

Connected through hyperedge HE-5 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "compressing graphs and indexes with recursive graph bisection"
    - "network structure inference a survey motivations methods and applications"
    - "efficient frequent directions algorithm for sparse matrices"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-6 (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"
    - "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"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-7 (induced by source paper "priime a generic framework for interactive personalized interesting pattern discovery"):
  with members:
    - "deepwalk online learning of social representations"
    - "going deeper with convolutions"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "summarizing data succinctly with the most informative itemsets"

Connected through hyperedge HE-8 (induced by source paper "generating realistic synthetic population datasets"):
  with members:
    - "comparing apples and oranges measuring differences between data mining results"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "finding good itemsets by packing data"
    - "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)