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 meta analysis of the anomaly detection problem"
  Abstract: This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem important to real-world applications: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply a representative set of anomaly detection algorithms to this corpus, yielding a very large collection of experimental results. We analyze these results to understand many phenomena observed in previous work. First we observe the effects of experimental design on experimental results. Second, results are evaluated with two metrics, ROC Area Under the Curve and Average Precision. We employ statistical hypothesis testing to demonstrate the value (or lack thereof) of our benchmarks. We then offer several approaches to summarizing our experimental results, drawing several conclusions about the impact of our methodology as well as the strengths and weaknesses of some algorithms. Last, we compare results against a trivial solution as an alternate means of normalizing the reported performance of algorithms. The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.

Connected through hyperedge HE-1 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "grasp2vec learning object representations from self supervised grasping"
    - "residual reinforcement learning for robot control"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "machine teaching a new paradigm for building machine learning systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-2 (induced by source paper "a benchmark for anomaly segmentation"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "learning rich features for image manipulation detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"
    - "deep anomaly detection with outlier exposure"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-3 (induced by source paper "should i raise the red flag a comprehensive survey of anomaly scoring methods toward mitigating false alarms"):
  with members:
    - "active anomaly detection via ensembles"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep learning for time series analysis"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-4 (induced by source paper "a comparative evaluation of novelty detection algorithms for discrete sequences"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "visual interpretability for deep learning a survey"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "a critical review of recurrent neural networks for sequence learning"
    - "effective approaches to attention based neural machine translation"
    - "visualizing and understanding recurrent networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "no free lunch but a cheaper supper a general framework for streaming anomaly detection"):
  with members:
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "feature learning from incomplete eeg with denoising autoencoder"

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.AI(Artificial Intelligence)