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 "semi supervised stochastic multi domain learning using variational inference"):
  Abstract: Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.

Includes members:
  Member "automatic language identification in texts a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dianet bert and hierarchical attention multi task learning of fine grained dialect")
  Member "implicit reparameterization gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks on probabilistic autoregressive forecasting models")
  Member "auto encoding variational neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior")
  Member "generative neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards generating long and coherent text with multi level latent variable models")
  Member "multi space variational encoder decoders for semi supervised labeled sequence transduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational template machine for data to text generation")
  Member "fixing a broken elbo":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "discovering discrete latent topics with neural variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "variational neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mixpoet diverse poetry generation via learning controllable mixed latent space")

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.CL(Computation and Language)