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: "a new local distance based outlier detection approach for scattered real world data"
  Abstract: Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel "Local Distance-based Outlier Factor" (LDOF) to measure the {outlier-ness} of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. Properties of LDOF are theoretically analysed including LDOF's lower bound and its false-detection probability, as well as parameter settings. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in our outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method top-n LDOF is more effective at detecting outliers in scattered data. It is also easier to set parameters, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets.

Connected through hyperedge HE-1 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "elki a large open source library for data analysis elki release 0 7 5 heidelberg"):
  with members:
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "semantic word clouds with background corpus normalization and t distributed stochastic neighbor embedding"
    - "an experimental exploration of marsaglia s xorshift generators scrambled"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "robust and efficient fuzzy c means clustering constrained on flexible sparsity"):
  with members:
    - "group k means"
    - "optimized cartesian k means"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "direct structural analysis of domains defined by point clouds"):
  with members:
    - "integrating cad and numerical analysis dirty geometry handling using the finite cell method"
    - "from geometric design to numerical analysis a direct approach using the finite cell method on constructive solid geometry"

Connected through hyperedge HE-5 (induced by source paper "clustering with outlier removal"):
  with members:
    - "consensus clustering an embedding perspective extension and beyond"
    - "efficient gan based anomaly detection"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "streaming active learning strategies for real life credit card fraud detection assessment and visualization"):
  with members:
    - "scarff a scalable framework for streaming credit card fraud detection with spark"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "learning representations for outlier detection on a budget"):
  with members:
    - "toward supervised anomaly detection"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "less is more building selective anomaly ensembles"):
  with members:
    - "data fusion resolving conflicts from multiple sources"
    - "graph based anomaly detection and description a survey"

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)