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: "mining local process models"
  Abstract: In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as local process models. Local process model mining can be positioned in-between process discovery and episode/sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode/sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.

Connected through hyperedge HE-1 (induced by source paper "towards intelligent robotic process automation for bpmers"):
  with members:
    - "automated discovery of process models from event logs review and benchmark"

Connected through hyperedge HE-2 (induced by source paper "discovering more precise process models from event logs by filtering out chaotic activities"):
  with members:
    - "rapidprom mine your processes and not just your data"
    - "event abstraction for process mining using supervised learning techniques"
    - "mining process model descriptions of daily life through event abstraction"

Connected through hyperedge HE-3 (induced by source paper "from low level events to activities a session based approach extended version"):
  with members:
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "event abstraction for process mining using supervised learning techniques"

Connected through hyperedge HE-4 (induced by source paper "prototype selection based on clustering and conformance metrics for model discovery"):
  with members:
    - "rapidprom mine your processes and not just your data"

Connected through hyperedge HE-5 (induced by source paper "interest driven discovery of local process models"):
  with members:
    - "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"

Connected through hyperedge HE-6 (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 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-7 (induced by source paper "mining process model descriptions of daily life through event abstraction"):
  with members:
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "heuristic approaches for generating local process models through log projections"
    - "log based evaluation of label splits for 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)