human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mechanisms for integrated feature normalization and remaining useful life estimation using lstms applied to hard disks"):
  Abstract: With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers such as hard disks, that are subject to degradation. A Deep Long-Short Term Memory (LSTM) Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model can be used to predict RUL for devices in different models from same manufacturer through transfer learning.

Includes members:
  Member "qdds a novel quantum swarm algorithm inspired by a double dirac delta potential":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chaotic quantum double delta swarm algorithm using chebyshev maps theoretical foundations performance analyses and convergence issues")
  Member "particle swarm optimization a survey of historical and recent developments with hybridization perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data driven optimization of public transit schedule")
  Member "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data driven optimization of public transit schedule")
  Member "predicting remaining useful life using time series embeddings based on recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comparative study between bayesian and frequentist neural networks for remaining useful life estimation in condition based maintenance")
  Member "lstm based encoder decoder for multi sensor anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structural deep clustering network")

Question: Please tell me which class the source paper 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)