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 visual reasoning without strong priors"
  Abstract: Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.

Connected through hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition"):
  with members:
    - "a two step disentanglement method"
    - "hologan unsupervised learning of 3d representations from natural images"
    - "foreground aware image inpainting"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "visual object networks image generation with disentangled 3d representation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "cr gan learning complete representations for multi view generation"
    - "multi task convolutional neural network for pose invariant face recognition"

Connected through hyperedge HE-2 (induced by source paper "exemplar normalization for learning deep representation"):
  with members:
    - "channel equilibrium networks for learning deep representation"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "attentive normalization"
    - "positional normalization"
    - "global second order pooling convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "neural pose transfer by spatially adaptive instance normalization"):
  with members:
    - "multi garment net learning to dress 3d people from images"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "multimodal unsupervised image to image translation"
    - "3d coded 3d correspondences by deep deformation"
    - "meshnet mesh neural network for 3d shape representation"
    - "which training methods for gans do actually converge"
    - "variational autoencoders for deforming 3d mesh models"

Connected through hyperedge HE-4 (induced by source paper "multimodal model agnostic meta learning via task aware modulation"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "on first order meta learning algorithms"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "meta continual learning"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-5 (induced by source paper "semantic image synthesis with spatially adaptive normalization"):
  with members:
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "group normalization"
    - "multimodal unsupervised image to image translation"
    - "manipulating attributes of natural scenes via hallucination"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-6 (induced by source paper "attentive normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "instance level meta normalization"
    - "image synthesis from reconfigurable layout and style"
    - "searching for mobilenetv3"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fixup initialization residual learning without normalization"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"

Connected through hyperedge HE-7 (induced by source paper "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "rgb t object tracking benchmark and baseline"
    - "modulating early visual processing by language"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-8 (induced by source paper "semantics disentangling for text to image generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient video object segmentation via network modulation"
    - "transferring gans generating images from limited data"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "large scale gan training for high fidelity natural image synthesis"
    - "image generation from scene graphs"

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)