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: "areas of attention for image captioning"
  Abstract: We propose "Areas of Attention", a novel attention-based model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attention-based approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic structure in the generated captions.

Connected through hyperedge HE-1 (induced by source paper "adaptive offline quintuplet loss for image text matching"):
  with members:
    - "expressing objects just like words recurrent visual embedding for image text matching"
    - "visual semantic reasoning for image text matching"
    - "saliency guided attention network for image sentence matching"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "hal improved text image matching by mitigating visual semantic hubs"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "identity aware textual visual matching with latent co attention"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-2 (induced by source paper "expressing objects just like words recurrent visual embedding for image text matching"):
  with members:
    - "position focused attention network for image text matching"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal relation and attention learning for facial action unit detection"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "deep structure inference network for facial action unit recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "recurrent attentional networks for saliency detection"

Connected through hyperedge HE-4 (induced by source paper "visual entailment a novel task for fine grained image understanding"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bilinear attention networks"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "visual reasoning by progressive module networks"
    - "grounded textual entailment"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-5 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"

Connected through hyperedge HE-6 (induced by source paper "m 2 meshed memory transformer for image captioning"):
  with members:
    - "hierarchy parsing for image captioning"
    - "image captioning transforming objects into words"
    - "augmenting self attention with persistent memory"
    - "attention on attention for image captioning"
    - "reflective decoding network for image captioning"
    - "videobert a joint model for video and language representation learning"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"

Connected through hyperedge HE-7 (induced by source paper "multi scale guided attention for medical image segmentation"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"
    - "an attention model for group level emotion recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"

Connected through hyperedge HE-8 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "graph rise graph regularized image semantic embedding"
    - "bottom up object detection by grouping extreme and center points"
    - "neural baby talk"
    - "dropblock a regularization method for convolutional networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"

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)