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: "mix and match networks encoder decoder alignment for zero pair image translation"
  Abstract: We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.

Connected through hyperedge HE-1 (induced by source paper "sdit scalable and diverse cross domain image translation"):
  with members:
    - "controlling biases and diversity in diverse image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "how good is my gan"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-2 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "depth aware cnn for rgb d segmentation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "image to image translation for cross domain disentanglement"

Connected through hyperedge HE-3 (induced by source paper "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-4 (induced by source paper "gmm unit unsupervised multi domain and multi modal image to image translation via attribute gaussian mixture modelling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "transgaga geometry aware unsupervised image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-5 (induced by source paper "controlling biases and diversity in diverse image to image translation"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "identifying and correcting label bias in machine learning"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "women also snowboard overcoming bias in captioning models"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "weakly supervised domain specific color naming based on attention"

Connected through hyperedge HE-6 (induced by source paper "cross modal variational auto encoder with distributed latent spaces and associators"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "learning to localize sound source in visual scenes"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cross modal deep variational hand pose estimation"
    - "multimodal generative models for scalable weakly supervised learning"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "conditional generation of multi modal data using constrained embedding space mapping"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-7 (induced by source paper "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "probabilistic plant modeling via multi view image to image translation"
    - "multimodal unsupervised image to image translation"
    - "stereoscopic neural style transfer"
    - "one shot unsupervised cross domain translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "conditional image to image translation"

Connected through hyperedge HE-8 (induced by source paper "transferring gans generating images from limited data"):
  with members:
    - "cgans with projection discriminator"
    - "pros and cons of gan evaluation measures"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

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.CV(Computer Vision and Pattern Recognition)