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: "significance of episodes based on minimal windows"
  Abstract: Discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. Traditionally, a level-wise approach is used to discover all frequent episodes. While this technique is computationally feasible it may result in a vast number of patterns, especially when low thresholds are used. In this paper we propose a new quality measure for episodes. We say that an episode is significant if the average length of its minimal windows deviates greatly when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step to test whether the discovered frequent episodes are truly interesting and consequently to reduce the number of output. As a main contribution we introduce a technique that allows us to compute the distribution of lengths of minimal windows using the independence model. Such a computation task is surpisingly complex and in order to solve it we compute the distribution iteratively starting from simple episodes and progressively moving towards the more complex ones. In our experiments we discover candidate episodes that have a sufficient amount of minimal windows and test each candidate for significance. The experimental results demonstrate that our approach finds significant episodes while ignoring uninteresting ones.

Connected through hyperedge HE-1 (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"
    - "tell me what i need to know succinctly summarizing data with itemsets"

Connected through hyperedge HE-2 (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"
    - "summarizing data succinctly with the most informative itemsets"
    - "using background knowledge to rank itemsets"
    - "opus an efficient admissible algorithm for unordered search"

Connected through hyperedge HE-3 (induced by source paper "keeping it short and simple summarising complex event sequences with multivariate patterns"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"
    - "mining closed episodes with simultaneous events"

Connected through hyperedge HE-4 (induced by source paper "discovering episodes with compact minimal windows"):
  with members:
    - "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-5 (induced by source paper "the long and the short of it summarising event sequences with serial episodes"):
  with members:
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-6 (induced by source paper "mining closed episodes with simultaneous events"):
  with members:
    - "mining closed strict episodes"

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)