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: "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
  Abstract: Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations, examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.

Connected through hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "deep semantic segmentation of natural and medical images a review"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "deeply supervised salient object detection with short connections"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"

Connected through hyperedge HE-2 (induced by source paper "pyodds an end to end outlier detection system with automated machine learning"):
  with members:
    - "deep structured cross modal anomaly detection"
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "pyod a python toolbox for scalable outlier detection"
    - "pyodds an end to end outlier detection system"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "repad real time proactive anomaly detection for time series"):
  with members:
    - "greenhouse a zero positive machine learning system for time series anomaly detection"
    - "automatic anomaly detection in the cloud via statistical learning"
    - "hybrid job driven scheduling for virtual mapreduce clusters"
    - "intrusion detection using continuous time bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "hardware architecture proposal for teda algorithm to data streaming anomaly detection"):
  with members:
    - "high performance parallel implementation of genetic algorithm on fpga"

Connected through hyperedge HE-5 (induced by source paper "a delay metric for video object detection what average precision fails to tell"):
  with members:
    - "step spatio temporal progressive learning for video action detection"
    - "minimum delay object detection from video"
    - "catdet cascaded tracked detector for efficient object detection from video"
    - "object detection in video with spatiotemporal sampling networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "optimizing video object detection via a scale time lattice"
    - "detect or track towards cost effective video object detection tracking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-6 (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"
    - "a meta analysis of the anomaly detection problem"
    - "systematic construction of anomaly detection benchmarks from real data"

Connected through hyperedge HE-7 (induced by source paper "rsm gan a convolutional recurrent gan for anomaly detection in contaminated seasonal multivariate time series"):
  with members:
    - "unsupervised learning of anomaly detection from contaminated image data using simultaneous encoder training"
    - "efficient gan based anomaly detection"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved training of wasserstein gans"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "sequential online prediction in the presence of outliers and change points an instant temporal structure learning approach"):
  with members:
    - "state space model based trust evaluation over wireless sensor networks an iterative particle filter approach"
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"

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)