human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "no free lunch but a cheaper supper a general framework for streaming anomaly detection"):
  Abstract: Over the past years, there has been an increased research interest in the problem of detecting anomalies in temporal streaming data. Among the many algorithms proposed every year, there exists no single general method that has been shown to outperform the others across different anomaly types, applications, and datasets. Furthermore, experimental studies conducted using existing methods lack reliability since they attempt to assess the superiority of the algorithms without studying their shared properties and the differences thoroughly. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection and provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing to isolate the effects of shared and unique characteristics of different algorithms on the detection performance. Using SAFARI, we have implemented different anomaly detectors and identified a research gap which motivates us to propose a novel learning strategy in this work. Then, we have conducted an extensive evaluation study on 20 detectors that are composed by SAFARI and compared their performances using real-world benchmark datasets with different properties. Finally, we have discussed their benefits and drawbacks in-depth and drawn a set of conclusions to guide future users of SAFARI.

Includes members:
  Member "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "a meta analysis of the anomaly detection problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network")
  Member "systematic construction of anomaly detection benchmarks from real data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network")
  Member "evaluating real time anomaly detection algorithms the numenta anomaly benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "feature learning from incomplete eeg with denoising autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")

Question: Please tell me which class the source paper 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)