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: "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
  Abstract: As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

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 "cratos cogination of reliable algorithm for time series optimal solution"):
  with members:
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"

Connected through hyperedge HE-3 (induced by source paper "a review on outlier anomaly detection in time series data"):
  with members:
    - "automatic anomaly detection in the cloud via statistical learning"

Connected through hyperedge HE-4 (induced by source paper "the machine learning bazaar harnessing the ml ecosystem for effective system development"):
  with members:
    - "neuronblocks building your nlp dnn models like playing lego"
    - "massively parallel hyperparameter tuning"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "metadata embeddings for user and item cold start recommendations"

Connected through hyperedge HE-5 (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"
    - "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"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-6 (induced by source paper "lstm based anomaly detection detection rules from extreme value theory"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "recurrent neural radio anomaly detection"

Connected through hyperedge HE-7 (induced by source paper "no free lunch but a cheaper supper a general framework for streaming anomaly detection"):
  with members:
    - "a meta analysis of the anomaly detection problem"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "sequential vae lstm for anomaly detection on time series"):
  with members:
    - "time series anomaly detection service at microsoft"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "adam a method for stochastic optimization"

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)