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: "xgbod improving supervised outlier detection with unsupervised representation learning"
  Abstract: A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.

Connected through hyperedge HE-1 (induced by source paper "suod a scalable unsupervised outlier detection framework"):
  with members:
    - "suod toward scalable unsupervised outlier detection"
    - "pyod a python toolbox for scalable outlier detection"
    - "optimal sparse decision trees"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "semi supervised rare disease detection using generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks"):
  with members:
    - "explaining anomalies in groups with characterizing subspace rules"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"

Connected through hyperedge HE-3 (induced by source paper "music artist classification with convolutional recurrent neural networks"):
  with members:
    - "lscp locally selective combination in parallel outlier ensembles"
    - "convolutional recurrent neural networks for bird audio detection"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "explaining deep convolutional neural networks on music classification"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "pyod a python toolbox for scalable outlier detection"):
  with members:
    - "dcso dynamic combination of detector scores for outlier ensembles"
    - "anomaly detection for an e commerce pricing system"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-5 (induced by source paper "dcso dynamic combination of detector scores for outlier ensembles"):
  with members:
    - "on clustering time series using euclidean distance and pearson correlation"
    - "sequential ensemble learning for outlier detection a bias variance perspective"
    - "local subspace based outlier detection using global neighbourhoods"
    - "learning representations for outlier detection on a budget"
    - "less is more building selective anomaly ensembles"

Connected through hyperedge HE-6 (induced by source paper "combining machine learning models using combo library"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "sync a unified framework for generating synthetic population with gaussian copula"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "less is more building selective anomaly ensembles"

Connected through hyperedge HE-8 (induced by source paper "lscp locally selective combination in parallel outlier ensembles"):
  with members:
    - "dcso dynamic combination of detector scores for outlier ensembles"
    - "pyod a python toolbox for scalable outlier detection"
    - "learning representations for outlier detection on a budget"

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)