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 "variational hybridization and transformation for large inaccurate noisy or networks"):
  Abstract: Variational inference provides approximations to the computationally intractable posterior distribution in Bayesian networks. A prominent medical application of noisy-or Bayesian network is to infer potential diseases given observed symptoms. Previous studies focus on approximating a handful of complicated pathological cases using variational transformation. Our goal is to use variational transformation as part of a novel hybridized inference for serving reliable and real time diagnosis at web scale. We propose a hybridized inference that allows variational parameters to be estimated without disease posteriors or priors, making the inference faster and much of its computation recyclable. In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks. In experiments, we perform comparative study on a large real life medical network and scalability study on a much larger (36,000x) synthesized network.

Includes members:
  Member "deep speech 2 end to end speech recognition in english and mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "addressing the rare word problem in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards a human like open domain chatbot")
  Member "a tractable inference algorithm for diagnosing multiple diseases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "unsupervised learning of noisy or bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network")
  Member "formula based probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic inference modulo theories")
  Member "structured priors for structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the indian chefs process")
  Member "variational probabilistic inference and the qmr dt network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the accuracy vs coverage trade off in patient facing diagnosis 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)