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: "real time anomaly detection for streaming analytics"
  Abstract: Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection for an e commerce pricing system"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "efficient gan based anomaly detection"
    - "applying deep learning to airbnb search"
    - "xgboost a scalable tree boosting system"
    - "deep structured energy based models for anomaly detection"
    - "sequential feature explanations for anomaly detection"
    - "adam a method for stochastic optimization"
    - "highly comparative feature based time series classification"

Connected through hyperedge HE-2 (induced by source paper "time series anomaly detection with variational autoencoders"):
  with members:
    - "an improvement of paa on trend based approximation for time series"
    - "adversarially learned anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "a survey on deep learning in medical image analysis"
    - "identifying and categorizing anomalies in retinal imaging data"

Connected through hyperedge HE-3 (induced by source paper "vedar accountable behavioural change detection"):
  with members:
    - "automatic anomaly detection in the cloud via statistical learning"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "detecting irregular patterns in iot streaming data for fall detection"):
  with members:
    - "online fall detection using recurrent neural networks"
    - "deep learning for iot big data and streaming analytics a survey"
    - "review of fall detection techniques a data availability perspective"

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)