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 "a simple baseline for bayesian uncertainty in deep learning"):
  Abstract: We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient descent (SGD) iterates with a modified learning rate schedule, has recently been shown to improve generalization in deep learning. With SWAG, we fit a Gaussian using the SWA solution as the first moment and a low rank plus diagonal covariance also derived from the SGD iterates, forming an approximate posterior distribution over neural network weights; we then sample from this Gaussian distribution to perform Bayesian model averaging. We empirically find that SWAG approximates the shape of the true posterior, in accordance with results describing the stationary distribution of SGD iterates. Moreover, we demonstrate that SWAG performs well on a wide variety of tasks, including out of sample detection, calibration, and transfer learning, in comparison to many popular alternatives including MC dropout, KFAC Laplace, SGLD, and temperature scaling.

Includes members:
  Member "cyclical stochastic gradient mcmc for bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "swalp stochastic weight averaging in low precision training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "subspace inference for bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "accurate uncertainties for deep learning using calibrated regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a financial service chatbot based on deep bidirectional transformers")
  Member "evaluating bayesian deep learning methods for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")
  Member "averaging weights leads to wider optima and better generalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "gradient descent happens in a tiny subspace":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling laws for neural language models")

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)