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: "clustering based predictive process monitoring"
  Abstract: Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The predicate can be, for example, a temporal logic constraint or a time constraint, or any predicate that can be evaluated over a completed trace. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous traces are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data to discriminate between fulfillments and violations. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.

Connected through hyperedge HE-1 (induced by source paper "exploiting event log event attributes in rnn based prediction"):
  with members:
    - "classifying process instances using recurrent neural networks"
    - "predictive process monitoring methods which one suits me best"
    - "structural feature selection for event logs"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (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"
    - "predictive monitoring of business processes"
    - "verification of relational data centric dynamic systems with external services"

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"
    - "predictive monitoring of business processes"

Connected through hyperedge HE-4 (induced by source paper "classifying process instances using recurrent neural networks"):
  with members:
    - "structural feature selection for event logs"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (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"
    - "predictive monitoring of business processes"
    - "verification of relational data centric dynamic systems with external services"

Connected through hyperedge HE-6 (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"
    - "predictive monitoring of business processes"

Connected through hyperedge HE-7 (induced by source paper "temporal stability in predictive process monitoring"):
  with members:
    - "predicting process behaviour using deep learning"
    - "xgboost a scalable tree boosting system"
    - "predictive monitoring of business processes"

Connected through hyperedge HE-8 (induced by source paper "foundations of prescriptive process monitoring"):
  with members:
    - "outcome oriented predictive process monitoring review and benchmark"
    - "predictive monitoring of business processes"
    - "types of cost in inductive concept learning"

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)