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: "summarizing data succinctly with the most informative itemsets"
  Abstract: Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would find interesting and/or surprising. Given new knowledge about the data, our expectations will change. Hence, in order to avoid redundant results, knowledge discovery algorithms ideally should follow such an iterative updating procedure.   With this in mind, we introduce a well-founded approach for succinctly summarizing data with the most informative itemsets; using a probabilistic maximum entropy model, we iteratively find the itemset that provides us the most novel information—that is, for which the frequency in the data surprises us the most—and in turn we update our model accordingly. As we use the maximum entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and nonredundant.   The algorithm that we present, called mtv, can either discover the top-k most informative itemsets, or we can employ either the Bayesian Information Criterion (bic) or the Minimum Description Length (mdl) principle to automatically identify the set of itemsets that together summarize the data well. In other words, our method will “tell you what you need to know” about the data. Importantly, it is a one-phase algorithm: rather than picking itemsets from a user-provided candidate set, itemsets and their supports are mined on-the-fly. To further its applicability, we provide an efficient method to compute the maximum entropy distribution using Quick Inclusion-Exclusion.   Experiments on our method, using synthetic, benchmark, and real 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 nonredundant itemsets.

Connected through hyperedge HE-1 (induced by source paper "mining insights from weakly structured event data"):
  with members:
    - "discovering more precise process models from event logs by filtering out chaotic activities"
    - "interest driven discovery of local process models"
    - "the imprecisions of precision measures in process mining"
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "heuristic approaches for generating local process models through log projections"
    - "conformance checking based on multi perspective declarative process models"
    - "event abstraction for process mining using supervised learning techniques"
    - "mining process model descriptions of daily life through event abstraction"

Connected through hyperedge HE-2 (induced by source paper "free rider episode screening via dual partition model"):
  with members:
    - "skopus mining top k sequential patterns under leverage"
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "ranking episodes using a partition model"
    - "discovering compressing serial episodes from event sequences"
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"

Connected through hyperedge HE-3 (induced by source paper "efficiently discovering locally exceptional yet globally representative subgroups"):
  with members:
    - "identifying consistent statements about numerical data with dispersion corrected subgroup discovery"
    - "inherent trade offs in the fair determination of risk scores"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "maximum entropy based significance of itemsets"

Connected through hyperedge HE-4 (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"
    - "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"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-5 (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"
    - "tell me what i need to know succinctly summarizing data with itemsets"

Connected through hyperedge HE-6 (induced by source paper "skopus mining top k sequential patterns under leverage"):
  with members:
    - "ranking episodes using a partition model"
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "using background knowledge to rank itemsets"
    - "significance of episodes based on minimal windows"
    - "opus an efficient admissible algorithm for unordered search"

Connected through hyperedge HE-7 (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"
    - "interesting multi relational patterns"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "introduction to machine learning class notes 67577"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-8 (induced by source paper "skopus exact discovery of the most interesting sequential patterns under leverage"):
  with members:
    - "ranking episodes using a partition model"
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "using background knowledge to rank 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)