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: "data driven model for the identification of the rock type at a drilling bit"
  Abstract: In order to bridge the gap of more than 15m between the drilling bit and high-fidelity rock type sensors during the directional drilling, we present a novel approach for identifying rock type at the drilling bit. The approach is based on application of machine learning techniques for Measurements While Drilling (MWD) data. We demonstrate capabilities of the developed approach for distinguishing between the rock types corresponding to (1) a target oil bearing interval of a reservoir and (2) a non-productive shale layer and compare it to more traditional physics-driven approaches. The dataset includes MWD data and lithology mapping along multiple wellbores obtained by processing of Logging While Drilling (LWD) measurements from a massive drilling effort on one of the major newly developed oilfield in the North of Western Siberia. We compare various machine-learning algorithms, examine extra features coming from physical modeling of drilling mechanics, and show that the classification error can be reduced from 13.5% to 9%.

Connected through hyperedge HE-1 (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"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "supervised speech separation based on deep learning an overview"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (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"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "an empirical evaluation of similarity measures for time series classification"

Connected through hyperedge HE-3 (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"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "an empirical evaluation of similarity measures for time series classification"

Connected through hyperedge HE-4 (induced by source paper "zhores petaflops supercomputer for data driven modeling machine learning and artificial intelligence installed in skolkovo institute of science and technology"):
  with members:
    - "deep image prior"
    - "parsing images of overlapping organisms with deep singling out networks"
    - "neural codes for image retrieval"

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)