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: "cu net coupled u nets"
  Abstract: We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling connections could make the information flow more efficiently across U-Nets. The feature reuse across U-Nets makes each U-Net very parameter efficient. We evaluate the coupled U-Nets on two benchmark datasets of human pose estimation. Both the accuracy and model parameter number are compared. The CU-Net obtains comparable accuracy as state-of-the-art methods. However, it only has at least 60% fewer parameters than other approaches.

Connected through hyperedge HE-1 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi scale structure aware network for human pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "knowledge guided deep fractal neural networks for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "efficient and accurate arbitrary shaped text detection with pixel aggregation network"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "selective kernel networks"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "salient objects in clutter bringing salient object detection to the foreground"

Connected through hyperedge HE-3 (induced by source paper "learning feature to feature translator by alternating back propagation for generative zero shot learning"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "learning where to look semantic guided multi attention localization for zero shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning to forecast and refine residual motion for image to video generation"
    - "cr gan learning complete representations for multi view generation"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "uu nets connecting discriminator and generator for image to image translation"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "training generative adversarial networks via turing test"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-5 (induced by source paper "learning to forecast and refine residual motion for image to video generation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "cr gan learning complete representations for multi view generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "pose guided person image generation"

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)