human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the role of minimal complexity functions in unsupervised learning of semantic mappings"
  Abstract: We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, in typical cases, it seems possible to build infinitely many alternative mappings from every target mapping. This apparent ambiguity stands in sharp contrast to the recent empirical success in solving this problem. #R##N#We identify the abstract notion of aligning two domains in a semantic way with concrete terms of minimal relative complexity. A theoretical framework for measuring the complexity of compositions of functions is developed in order to show that it is reasonable to expect the minimal complexity mapping to be unique. The measured complexity used is directly related to the depth of the neural networks being learned and a semantically aligned mapping could then be captured simply by learning using architectures that are not much bigger than the minimal architecture. #R##N#Various predictions are made based on the hypothesis that semantic alignment can be captured by the minimal mapping. These are verified extensively. In addition, a new mapping algorithm is proposed and shown to lead to better mapping results.

Connected through hyperedge HE-1 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-2 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "video to video synthesis"
    - "neural autoregressive flows"
    - "universal neural machine translation for extremely low resource languages"
    - "multimodal unsupervised image to image translation"
    - "neural ordinary differential equations"
    - "generalization bounds for unsupervised cross domain mapping with wgans"

Connected through hyperedge HE-3 (induced by source paper "domain intersection and domain difference"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "optimal unsupervised domain translation"):
  with members:
    - "latent translation crossing modalities by bridging generative models"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-5 (induced by source paper "multimodal unsupervised image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a closed form solution to photorealistic image stylization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "multi agent diverse generative adversarial networks"
    - "style transfer from non parallel text by cross alignment"
    - "decomposing motion and content for natural video sequence prediction"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "instagan instance aware image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "weakly supervised instance segmentation using class peak response"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep sets"
    - "improving shape deformation in unsupervised image to image translation"

Connected through hyperedge HE-7 (induced by source paper "sem gan semantically consistent image to image translation"):
  with members:
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "deep visual domain adaptation a survey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "triangle generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "unsupervised domain adaptation for semantic segmentation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised correlation analysis"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised learning of semantic mappings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "one sided unsupervised domain mapping"
    - "word translation without parallel data"
    - "dualgan unsupervised dual learning for image to image translation"

Question: Please tell me which class the center node 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)