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: "unsupervised event abstraction using pattern abstraction and local process models"
  Abstract: Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.

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"
    - "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"
    - "a global constraint for mining sequential patterns with gap constraint"

Connected through hyperedge HE-2 (induced by source paper "from low level events to activities a session based approach extended version"):
  with members:
    - "mining local process models"
    - "event abstraction for process mining using supervised learning techniques"

Connected through hyperedge HE-3 (induced by source paper "mining non redundant local process models from sequence databases"):
  with members:
    - "heuristic approaches for generating local process models through log projections"
    - "mining local process models"
    - "mining process model descriptions of daily life through event abstraction"
    - "the long and the short of it summarising event sequences with serial episodes"

Connected through hyperedge HE-4 (induced by source paper "mining process model descriptions of daily life through event abstraction"):
  with members:
    - "heuristic approaches for generating local process models through log projections"
    - "log based evaluation of label splits for process models"
    - "mining local process models"
    - "event abstraction for process mining using supervised learning techniques"

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.DB(Databases)