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: "semantic interpolation in implicit models"
  Abstract: In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on code vectors with the geometry of the interpolating paths. Otherwise, typical assumptions about the quality and semantics of in-between points may not be justified. Based on our analysis we propose to modify the prior code distribution to put significantly more probability mass closer to the origin. As a result, linear interpolation paths are not only shortest paths, but they are also guaranteed to pass through high-density regions, irrespective of the dimensionality of the latent space. Experiments on standard benchmark image datasets demonstrate clear visual improvements in the quality of the generated samples and exhibit more meaningful interpolation paths.

Connected through hyperedge HE-1 (induced by source paper "controlling style and semantics in weakly supervised image generation"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "chatpainter improving text to image generation using dialogue"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"

Connected through hyperedge HE-2 (induced by source paper "non parametric priors for generative adversarial networks"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "interpolation in generative models"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "neural photo editing with introspective adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generating sentences from a continuous space"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "realism index interpolation in generative models with arbitrary prior"):
  with members:
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "neural photo editing with introspective adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generating sentences from a continuous space"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (induced by source paper "on latent distributions without finite mean in generative models"):
  with members:
    - "sampling generative networks"
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"
    - "efficient estimation of word representations in vector space"

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)