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 "unsupervised multi target domain adaptation an information theoretic approach"):
  Abstract: Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domain-specific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.

Includes members:
  Member "task discriminative domain alignment for unsupervised domain adaptation":
    also connected through other hyperedges:
      (none)
  Member "unsupervised visual domain adaptation a deep max margin gaussian process approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "entropy minimization vs diversity maximization for domain adaptation")
  Member "moment matching for multi source domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "stargan vc non parallel many to many voice conversion with star generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "voice conversion for whispered speech synthesis")
  Member "sample to sample correspondence for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a two stage approach to few shot learning for image recognition")
  Member "uncertainty in the variational information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dueling decoders regularizing variational autoencoder latent spaces")
  Member "unsupervised domain adaptation using regularized hyper graph matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a two stage approach to few shot learning for image recognition")
  Member "mixgan learning concepts from different domains for mixture generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image processing using multi code gan prior")

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.CV(Computer Vision and Pattern Recognition)