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: "lstm networks for data aware remaining time prediction of business process instances"
  Abstract: Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.

Connected through hyperedge HE-1 (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"
    - "the internet of things meets business process management mutual benefits and challenges"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "clustering based predictive process monitoring"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (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"
    - "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"
    - "predictive monitoring of business processes"

Connected through hyperedge HE-5 (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"
    - "clustering based predictive process monitoring"
    - "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-6 (induced by source paper "memory augmented neural networks for predictive process analytics"):
  with members:
    - "end to end memory networks"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)