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: "lscp locally selective combination in parallel outlier ensembles"
  Abstract: In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses the issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly selected feature subspaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with seven widely used parallel frameworks. Experimental results demonstrate that one of these variants, LSCP_AOM, consistently outperforms baselines on the majority of twenty real-world 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"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"
    - "optimal sparse decision trees"
    - "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 "ensemble grammar induction for detecting anomalies in time series"):
  with members:
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-3 (induced by source paper "music artist classification with convolutional recurrent neural networks"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "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"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "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 "combining machine learning models using combo library"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-6 (induced by source paper "sync a unified framework for generating synthetic population with gaussian copula"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"
    - "less is more building selective anomaly ensembles"

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)