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: "learning generative models with visual attention"
  Abstract: Attention has long been proposed by psychologists as important for effectively dealing with the enormous sensory stimulus available in the neocortex. Inspired by the visual attention models in computational neuroscience and the need of object-centric data for generative models, we describe for generative learning framework using attentional mechanisms. Attentional mechanisms can propagate signals from region of interest in a scene to an aligned canonical representation, where generative modeling takes place. By ignoring background clutter, generative models can concentrate their resources on the object of interest. Our model is a proper graphical model where the 2D Similarity transformation is a part of the top-down process. A ConvNet is employed to provide good initializations during posterior inference which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our model can robustly attend to face regions of novel test subjects. More importantly, our model can learn generative models of new faces from a novel dataset of large images where the face locations are not known.

Connected through hyperedge HE-1 (induced by source paper "citation recommendations considering content and structural context embedding"):
  with members:
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-3 (induced by source paper "attentionrnn a structured spatial attention mechanism"):
  with members:
    - "modular generative adversarial networks"
    - "explainable neural computation via stack neural module networks"
    - "cbam convolutional block attention module"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "visual reference resolution using attention memory for visual dialog"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-4 (induced by source paper "pdanet polarity consistent deep attention network for fine grained visual emotion regression"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "cbam convolutional block attention module"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "squeeze and excitation networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-5 (induced by source paper "joint spatial and layer attention for convolutional networks"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "convolutional networks with adaptive inference graphs"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "towards visually explaining variational autoencoders"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "sliced wasserstein generative models"
    - "deep learning for anomaly detection a survey"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "re identification with consistent attentive siamese networks"
    - "sharpen focus learning with attention separability and consistency"

Connected through hyperedge HE-7 (induced by source paper "self attention capsule networks for object classification"):
  with members:
    - "quantitative phase imaging and artificial intelligence a review"
    - "brain tumor type classification via capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"
    - "attention u net learning where to look for the pancreas"
    - "dense and diverse capsule networks making the capsules learn better"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "factual or emotional stylized image captioning with adaptive learning and attention"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "perceptual losses for real time style transfer and super resolution"
    - "image captioning with semantic attention"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

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)