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: "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
  Abstract: Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.

Connected through hyperedge HE-1 (induced by source paper "security privacy in iot using machine learning blockchain threats countermeasures"):
  with members:
    - "machine learning in iot security current solutions and future challenges"
    - "security services using blockchains a state of the art survey"
    - "a survey on the security of blockchain systems"
    - "secure mobile crowdsensing with deep learning"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "a first look at identity management schemes on the blockchain"
    - "blockchain as a service an autonomous privacy preserving decentralized architecture for deep learning"
    - "sok security and privacy in machine learning"

Connected through hyperedge HE-2 (induced by source paper "multi objective variational autoencoder an application for smart infrastructure maintenance"):
  with members:
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "made masked autoencoder for distribution estimation"
    - "an autoencoder approach to learning bilingual word representations"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "gan augmented text anomaly detection with sequences of deep statistics"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"

Connected through hyperedge HE-4 (induced by source paper "real time data driven detection of the rock type alteration during a directional drilling"):
  with members:
    - "application of machine learning to accidents detection at directional drilling"
    - "data driven model for the identification of the rock type at a drilling bit"
    - "supervised speech separation based on deep learning an overview"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "application of machine learning to accidents detection at directional drilling"):
  with members:
    - "real time data driven detection of the rock type alteration during a directional drilling"
    - "data driven model for the identification of the rock type at a drilling bit"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "an empirical evaluation of similarity measures for time series classification"

Connected through hyperedge HE-6 (induced by source paper "failures detection at directional drilling using real time analogues search"):
  with members:
    - "real time data driven detection of the rock type alteration during a directional drilling"
    - "data driven model for the identification of the rock type at a drilling bit"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "an empirical evaluation of similarity measures for time series classification"

Connected through hyperedge HE-7 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "wireless interference identification with convolutional neural networks"

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)