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: "automated discovery of process models from event logs review and benchmark"
  Abstract: Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

Connected through hyperedge HE-1 (induced by source paper "towards intelligent robotic process automation for bpmers"):
  with members:
    - "mining local process models"

Connected through hyperedge HE-2 (induced by source paper "automated discovery of data transformations for robotic process automation"):
  with members:
    - "a survey on preprocessing methods for web usage data"

Connected through hyperedge HE-3 (induced by source paper "decay replay mining to predict next process events"):
  with members:
    - "tackling climate change with machine learning"
    - "process mining of programmable logic controllers input output event logs"
    - "deep learning a critical appraisal"
    - "memory augmented neural networks for predictive process analytics"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "the internet of things meets business process management mutual benefits and challenges"

Connected through hyperedge HE-4 (induced by source paper "dream nap decay replay mining to predict next process activities"):
  with members:
    - "deep learning a critical appraisal"
    - "memory augmented neural networks for predictive process analytics"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "the internet of things meets business process management mutual benefits and challenges"
    - "understanding deep learning requires rethinking generalization"
    - "time and activity sequence prediction of business process instances"

Connected through hyperedge HE-5 (induced by source paper "anti alignments measuring the precision of process models and event logs"):
  with members:
    - "the reachability problem for petri nets is not elementary"
    - "the imprecisions of precision measures in process mining"
    - "complexity of universality and related problems for partially ordered nfas"

Connected through hyperedge HE-6 (induced by source paper "secure multi party computation for inter organizational process mining"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "event based failure prediction in distributed business processes"

Connected through hyperedge HE-7 (induced by source paper "behavioral petri net mining and automated analysis for human computer interaction recommendations in multi application environments"):
  with members:
    - "towards modern inclusive factories a methodology for the development of smart adaptive human machine interfaces"

Connected through hyperedge HE-8 (induced by source paper "process mining of programmable logic controllers input output event logs"):
  with members:
    - "dream nap decay replay mining to predict next process activities"

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.SE(Software Engineering)