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: "ranking episodes using a partition model"
  Abstract: One of the biggest setbacks in traditional frequent pattern mining is that overwhelmingly many of the discovered patterns are redundant. A prototypical example of such redundancy is a freerider pattern where the pattern contains a true pattern and some additional noise events. A technique for filtering freerider patterns that has proved to be efficient in ranking itemsets is to use a partition model where a pattern is divided into two subpatterns and the observed support is compared to the expected support under the assumption that these two subpatterns occur independently. In this paper we develop a partition model for episodes, patterns discovered from sequential data. An episode is essentially a set of events, with possible restrictions on the order of events. Unlike with itemset mining, computing the expected support of an episode requires surprisingly sophisticated methods. In order to construct the model, we partition the episode into two subepisodes. We then model how likely the events in each subepisode occur close to each other. If this probability is high--which is often the case if the subepisode has a high support--then we can expect that when one event from a subepisode occurs, then the remaining events occur also close by. This approach increases the expected support of the episode, and if this increase explains the observed support, then we can deem the episode uninteresting. We demonstrate in our experiments that using the partition model can effectively and efficiently reduce the redundancy in episodes.

Connected through hyperedge HE-1 (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"
    - "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"
    - "summarizing data succinctly with the most informative itemsets"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"

Connected through hyperedge HE-2 (induced by source paper "proceedings of the 2017 siam international conference on data mining"):
  with members:
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-3 (induced by source paper "skopus mining top k sequential patterns under leverage"):
  with members:
    - "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"
    - "significance of episodes based on minimal windows"
    - "opus an efficient admissible algorithm for unordered search"

Connected through hyperedge HE-4 (induced by source paper "skopus exact discovery of the most interesting sequential patterns under leverage"):
  with members:
    - "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"

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)