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 process model descriptions of daily life through event abstraction"
  Abstract: Methods from the area of Process Mining traditionally focus on extracting insight in business processes from event logs. In this paper we explore the potential of Process Mining to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. Events in smart home environments are recorded at the level of sensor triggers, which is too low to mine habit-related behavioral patterns. Process discovery algorithms produce then overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework to automatically abstract sensor-level events to their interpretation at the human activity level. Our framework is based on the XES IEEE standard for event logs. We use supervised learning techniques to train it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction improve on precision as well as on F-score.

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"
    - "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"
    - "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:
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "heuristic approaches for generating local process models through log projections"
    - "mining local process models"
    - "the long and the short of it summarising event sequences with serial episodes"

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)