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: "time and activity sequence prediction of business process instances"
  Abstract: The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. #R##N#Many approaches have been proposed to cope with this problem but all of them assume that the underling process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which outperforms the state-of-the-art and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on two real case studies.

Connected through hyperedge HE-1 (induced by source paper "workload prediction of business processes an approach based on process mining and recurrent neural networks"):
  with members:
    - "classifying process instances using recurrent neural networks"

Connected through hyperedge HE-2 (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"
    - "automated discovery of process models from event logs review and benchmark"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"

Connected through hyperedge HE-3 (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"
    - "automated discovery of process models from event logs review and benchmark"
    - "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"

Connected through hyperedge HE-4 (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"
    - "theano a python framework for fast computation of mathematical expressions"
    - "clustering based predictive process monitoring"
    - "predictive monitoring of business processes"
    - "verification of relational data centric dynamic systems with external services"

Connected through hyperedge HE-5 (induced by source paper "prediction based resource allocation using bayesian neural networks and minimum cost and maximum flow algorithm"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "clustering based predictive process monitoring"
    - "predictive monitoring of business processes"

Connected through hyperedge HE-7 (induced by source paper "specification driven multi perspective predictive business process monitoring extended version"):
  with members:
    - "predicting process behaviour using deep learning"
    - "verification of generalized inconsistency aware knowledge and action bases extended version"
    - "clustering based predictive process monitoring"
    - "predictive monitoring of business processes"
    - "verification of relational data centric dynamic systems with external services"

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"
    - "clustering based predictive process monitoring"
    - "predictive monitoring of business processes"

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.AI(Artificial Intelligence)