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: "lstm based encoder decoder for multi sensor anomaly detection"
  Abstract: Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two real-world engine datasets with both predictive and unpredictable behavior. We show that EncDec-AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).

Connected through hyperedge HE-1 (induced by source paper "structural deep clustering network"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "deep clustering for unsupervised learning of visual features"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "deep subspace clustering networks"
    - "semi supervised classification with graph convolutional networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-2 (induced by source paper "drocc deep robust one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "anomaly detection with multiple hypotheses predictions"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-3 (induced by source paper "text a 3 activation anomaly analysis"):
  with members:
    - "dla dense layer analysis for adversarial example detection"
    - "deep learning for anomaly detection a survey"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-4 (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"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-5 (induced by source paper "waveletfcnn a deep time series classification model for wind turbine blade icing detection"):
  with members:
    - "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"
    - "are generative deep models for novelty detection truly better"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "wavelet convolutional neural networks"
    - "fast and accurate time series classification with weasel"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-6 (induced by source paper "learning competitive and discriminative reconstructions for anomaly detection"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"
    - "deep structured energy based models for anomaly detection"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "very deep convolutional networks for large scale image recognition"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "visual analytics of anomalous user behaviors a survey"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "the spread of low credibility content by social bots"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "graph based anomaly detection and description a survey"
    - "a fraud detection visualization system utilizing radial drawings and heat maps"
    - "occupational fraud detection through visualization"

Connected through hyperedge HE-8 (induced by source paper "binet multi perspective business process anomaly classification"):
  with members:
    - "analyzing business process anomalies using autoencoders"
    - "predicting process behaviour using deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "plg2 multiperspective processes randomization and simulation for online and offline settings"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"

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)