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: "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
  Abstract: We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets. Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters. Our code and datasets are available at this https URL or this https URL.

Connected through hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "local class specific and global image level generative adversarial networks for semantic guided scene generation"
    - "example guided style consistent image synthesis from semantic labeling"
    - "asymmetric generative adversarial networks for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "attentiongan unpaired image to image translation using attention guided generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "joint discriminative and generative learning for person re identification"
    - "consistency regularization for generative adversarial networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking"):
  with members:
    - "gridmask data augmentation"
    - "reppoints point set representation for object detection"
    - "res2net a new multi scale backbone architecture"
    - "foveabox beyond anchor based object detector"
    - "making convolutional networks shift invariant again"
    - "region proposal by guided anchoring"
    - "mixconv mixed depthwise convolutional kernels"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-4 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "travelgan image to image translation by transformation vector learning"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "patch based image inpainting with generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "unpaired image to image translation using adversarial consistency loss"):
  with members:
    - "breaking the cycle colleagues are all you need"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised attention guided image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "combogan unrestrained scalability for image domain translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-7 (induced by source paper "breaking the cycle colleagues are all you need"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (induced by source paper "let s get dirty gan based data augmentation for soiling and adverse weather classification in autonomous driving"):
  with members:
    - "fisheyemultinet real time multi task learning architecture for surround view automated parking system"
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "single image deraining a comprehensive benchmark analysis"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "semantic image synthesis with spatially adaptive normalization"

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)