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: "learning representations for outlier detection on a budget"
  Abstract: The problem of detecting a small number of outliers in a large dataset is an important task in many fields from fraud detection to high-energy physics. Two approaches have emerged to tackle this problem: unsupervised and supervised. Supervised approaches require a sufficient amount of labeled data and are challenged by novel types of outliers and inherent class imbalance, whereas unsupervised methods do not take advantage of available labeled training examples and often exhibit poorer predictive performance. We propose BORE (a Bagged Outlier Representation Ensemble) which uses unsupervised outlier scoring functions (OSFs) as features in a supervised learning framework. BORE is able to adapt to arbitrary OSF feature representations, to the imbalance in labeled data as well as to prediction-time constraints on computational cost. We demonstrate the good performance of BORE compared to a variety of competing methods in the non-budgeted and the budgeted outlier detection problem on 12 real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "dcso dynamic combination of detector scores for outlier ensembles"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "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"
    - "less is more building selective anomaly ensembles"

Connected through hyperedge HE-2 (induced by source paper "xgbod improving supervised outlier detection with unsupervised representation learning"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "supervised and unsupervised ensembling for knowledge base population"
    - "less is more building selective anomaly ensembles"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "lscp locally selective combination in parallel outlier ensembles"):
  with members:
    - "dcso dynamic combination of detector scores for outlier ensembles"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"

Connected through hyperedge HE-4 (induced by source paper "graph based selective outlier ensembles"):
  with members:
    - "a weighted correlation index for rankings with ties"

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)