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 "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  Abstract: Time series forecasting is one of the challenging problems for humankind. The traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses, as well as, to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M), where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.

Includes members:
  Member "phaselink a deep learning approach to seismic phase association":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extracting dispersion curves from ambient noise correlations using deep learning")
  Member "predicting inpatient discharge prioritization with electronic health records":
    also connected through other hyperedges:
      (none)
  Member "deepdownscale a deep learning strategy for high resolution weather forecast":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provenance data in the machine learning lifecycle in computational science and engineering")
  Member "anomaly detection with generative adversarial networks for multivariate time series":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder")
  Member "voice impersonation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion")
  Member "cred a deep residual network of convolutional and recurrent units for earthquake signal detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural network applications in earthquake prediction 1994 2019 meta analytic insight on their limitations")
  Member "svsgan singing voice separation via generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis")
  Member "adversarial learning for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non autoregressive neural dialogue generation")

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)