human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mining insights from weakly structured event data"):
  Abstract: This thesis focuses on process mining on event data where such a normative specification is absent and, as a result, the event data is less structured. The thesis puts special emphasis on one application domain that fits this description: the analysis of smart home data where sequences of daily activities are recorded. In this thesis we propose a set of techniques to analyze such data, which can be grouped into two categories of techniques. The first category of methods focuses on preprocessing event logs in order to enable process discovery techniques to extract insights into unstructured event data. In this category we have developed the following techniques: - An unsupervised approach to refine event labels based on the time at which the event took place, allowing for example to distinguish recorded eating events into breakfast, lunch, and dinner. - An approach to detect and filter from event logs so-called chaotic activities, which are activities that cause process discovery methods to overgeneralize. - A supervised approach to abstract low-level events into more high-level events, where we show that there exist situations where process discovery approaches overgeneralize on the low-level event data but are able to find precise models on the high-level event data. The second category focuses on mining local process models, i.e., collections of process model patterns that each describe some frequent pattern, in contrast to the single global process model that is obtained with existing process discovery techniques. Several techniques are introduced in the area of local process model mining, including a basic method, fast but approximate heuristic methods, and constraint-based techniques.

Includes members:
  Member "discovering more precise process models from event logs by filtering out chaotic activities":
    also connected through other hyperedges:
      (none)
  Member "interest driven discovery of local process models":
    also connected through other hyperedges:
      (none)
  Member "the imprecisions of precision measures in process mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anti alignments measuring the precision of process models and event logs")
  Member "unsupervised event abstraction using pattern abstraction and local process models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from low level events to activities a session based approach extended version")
  Member "heuristic approaches for generating local process models through log projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interest driven discovery of local process models")
  Member "conformance checking based on multi perspective declarative process models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interest driven discovery of local process models")
  Member "event abstraction for process mining using supervised learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discovering more precise process models from event logs by filtering out chaotic activities")
  Member "mining process model descriptions of daily life through event abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discovering more precise process models from event logs by filtering out chaotic activities")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)