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: "event abstraction for process mining using supervised learning techniques"
  Abstract: Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or process models that are not representative of the event log. We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity. This gives rise to the challenge to bridge the gap between an original low-level event log and a desired high-level perspective on this log, such that a more structured or more comprehensible process model can be discovered. We show that supervised learning can be leveraged for the event abstraction task when annotations with high-level interpretations of the low-level events are available for a subset of the sequences (i.e., traces). We present a method to generate feature vector representations of events based on XES extensions, and describe an approach to abstract events in an event log with Condition Random Fields using these event features. Furthermore, we propose a sequence-focused metric to evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. We conclude this paper by demonstrating the usefulness of supervised event abstraction for obtaining more structured and/or more comprehensible process models using both real life event data and synthetic event data.

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"
    - "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 "discovering more precise process models from event logs by filtering out chaotic activities"):
  with members:
    - "rapidprom mine your processes and not just your data"
    - "mining local process models"
    - "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"
    - "mining local process models"

Connected through hyperedge HE-4 (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"
    - "mining local process models"

Connected through hyperedge HE-5 (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"
    - "mining local process models"

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.LG(Machine Learning)