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: "predictive monitoring of business processes"
  Abstract: Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital.

Connected through hyperedge HE-1 (induced by source paper "specification driven predictive business process monitoring"):
  with members:
    - "predictive process monitoring methods which one suits me best"
    - "predicting process behaviour using deep learning"
    - "verification of data aware business processes in the presence of ontologies"
    - "time and activity sequence prediction of business process instances"
    - "theano a python framework for fast computation of mathematical expressions"
    - "clustering based predictive process monitoring"
    - "verification of relational data centric dynamic systems with external services"

Connected through hyperedge HE-2 (induced by source paper "fire now fire later alarm based systems for prescriptive process monitoring"):
  with members:
    - "outcome oriented predictive process monitoring review and benchmark"

Connected through hyperedge HE-3 (induced by source paper "survey and cross benchmark comparison of remaining time prediction methods in business process monitoring"):
  with members:
    - "temporal stability in predictive process monitoring"
    - "predicting process behaviour using deep learning"
    - "outcome oriented predictive process monitoring review and benchmark"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "xgboost a scalable tree boosting system"
    - "time and activity sequence prediction of business process instances"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "clustering based predictive process monitoring"

Connected through hyperedge HE-4 (induced by source paper "specification driven multi perspective predictive business process monitoring extended version"):
  with members:
    - "predicting process behaviour using deep learning"
    - "time and activity sequence prediction of business process instances"
    - "verification of generalized inconsistency aware knowledge and action bases extended version"
    - "clustering based predictive process monitoring"
    - "verification of relational data centric dynamic systems with external services"

Connected through hyperedge HE-5 (induced by source paper "incremental predictive process monitoring how to deal with the variability of real environments"):
  with members:
    - "predicting process behaviour using deep learning"
    - "outcome oriented predictive process monitoring review and benchmark"
    - "clustering based predictive process monitoring"

Connected through hyperedge HE-6 (induced by source paper "temporal stability in predictive process monitoring"):
  with members:
    - "predicting process behaviour using deep learning"
    - "xgboost a scalable tree boosting system"
    - "clustering based predictive process monitoring"

Connected through hyperedge HE-7 (induced by source paper "foundations of prescriptive process monitoring"):
  with members:
    - "outcome oriented predictive process monitoring review and benchmark"
    - "clustering based predictive process monitoring"
    - "types of cost in inductive concept learning"

Connected through hyperedge HE-8 (induced by source paper "predictive process monitoring methods which one suits me best"):
  with members:
    - "predicting process behaviour using deep learning"
    - "time and activity sequence prediction of business process instances"
    - "clustering based predictive process monitoring"

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)