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: "the long and the short of it summarising event sequences with serial episodes"
  Abstract: An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard frequent pattern miners do not achieve this goal, as due to the pattern explosion typically very large numbers of highly redundant patterns are returned.   We pursue the ideal for sequential data, by employing a pattern set mining approach - an approach where, instead of ranking patterns individually, we consider results as a whole. Pattern set mining has been successfully applied to transactional data, but has been surprisingly understudied for sequential data.   In this paper, we employ the MDL principle to identify the set of sequential patterns that summarises the data best. In particular, we formalise how to encode sequential data using sets of serial episodes, and use the encoded length as a quality score. As search strategy, we propose two approaches: the first algorithm selects a good pattern set from a large candidate set, while the second is a parameter-free any-time algorithm that mines pattern sets directly from the data. Experimentation on synthetic and real data demonstrates we efficiently discover small sets of informative patterns.

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 "a survey of parallel sequential pattern mining"):
  with members:
    - "a survey of utility oriented pattern mining"
    - "privacy preserving utility mining a survey"
    - "desq frequent sequence mining with subsequence constraints"
    - "accelerating dna sequence analysis using intel xeon phi"

Connected through hyperedge HE-3 (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"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-4 (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"
    - "summarizing data succinctly with the most informative itemsets"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"

Connected through hyperedge HE-5 (induced by source paper "learning latent events from network message logs a decomposition based approach"):
  with members:
    - "tensor decompositions for learning latent variable models"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-6 (induced by source paper "learning latent events from network message logs"):
  with members:
    - "tensor decompositions for learning latent variable models"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-7 (induced by source paper "mining non redundant local process models from sequence databases"):
  with members:
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "heuristic approaches for generating local process models through log projections"
    - "mining local process models"
    - "mining process model descriptions of daily life through event abstraction"

Connected through hyperedge HE-8 (induced by source paper "lexis an optimization framework for discovering the hierarchical structure of sequential data"):
  with members:
    - "the evolutionary origins of hierarchy"
    - "data representation and compression using linear programming approximations"
    - "identifying hierarchical structure in sequences a linear time algorithm"

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)