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: "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"
  Abstract: Abstract   Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.

Connected through hyperedge HE-1 (induced by source paper "a hybrid machine learning framework for analyzing human decision making through learning preferences"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "an interpretable machine learning framework for modelling human decision behavior"):
  with members:
    - "wide deep learning for recommender systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "similarity encoding for learning with dirty categorical variables"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "deep embedding forest forest based serving with deep embedding features"
    - "accelerating innovation through analogy mining"

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)