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: "less is more building selective anomaly ensembles"
  Abstract: Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a new ensemble approach for anomaly mining, with application to event detection in temporal graphs. Our method aims to combine results from heterogeneous detectors with varying outputs, and leverage the evidence from multiple sources to yield better performance. However, trusting all the results may deteriorate the overall ensemble accuracy, as some detectors may fall short and provide inaccurate results depending on the nature of the data in hand. This suggests that being selective in which results to combine is vital in building effective ensembles---hence "less is more". #R##N#In this paper we propose SELECT; an ensemble approach for anomaly mining that employs novel techniques to automatically and systematically select the results to assemble in a fully unsupervised fashion. We apply our method to event detection in temporal graphs, where SELECT successfully utilizes five base detectors and seven consensus methods under a unified ensemble framework. We provide extensive quantitative evaluation of our approach on five real-world datasets (four with ground truth), including Enron email communications, New York Times news corpus, and World Cup 2014 Twitter news feed. Thanks to its selection mechanism, SELECT yields superior performance compared to individual detectors alone, the full ensemble (naively combining all results), and an existing diversity-based ensemble.

Connected through hyperedge HE-1 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "pyod a python toolbox for scalable outlier detection"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "are generative deep models for novelty detection truly better"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "generative adversarial active learning for unsupervised outlier detection"

Connected through hyperedge HE-2 (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"
    - "learning representations for outlier detection on a budget"

Connected through hyperedge HE-3 (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"
    - "learning representations for outlier detection on a budget"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (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"
    - "lscp locally selective combination in parallel outlier ensembles"

Connected through hyperedge HE-5 (induced by source paper "midas microcluster based detector of anomalies in edge streams"):
  with members:
    - "densealert incremental dense subtensor detection in tensor streams"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-6 (induced by source paper "a flexible framework for anomaly detection via dimensionality reduction"):
  with members:
    - "anomaly detection in the bitcoin system a network perspective"

Connected through hyperedge HE-7 (induced by source paper "learning representations of ultrahigh dimensional data for random distance based outlier detection"):
  with members:
    - "winner take all autoencoders"
    - "representation learning a review and new perspectives"
    - "adadelta an adaptive learning rate method"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "using intuitionistic fuzzy set for anomaly detection of network traffic from flow interaction"):
  with members:
    - "internet anomaly detection based on complex network path"

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.DB(Databases)