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: "seeing with humans gaze assisted neural image captioning"
  Abstract: Gaze reflects how humans process visual scenes and is therefore increasingly used in computer vision systems. Previous works demonstrated the potential of gaze for object-centric tasks, such as object localization and recognition, but it remains unclear if gaze can also be beneficial for scene-centric tasks, such as image captioning. We present a new perspective on gaze-assisted image captioning by studying the interplay between human gaze and the attention mechanism of deep neural networks. Using a public large-scale gaze dataset, we first assess the relationship between state-of-the-art object and scene recognition models, bottom-up visual saliency, and human gaze. We then propose a novel split attention model for image captioning. Our model integrates human gaze information into an attention-based long short-term memory architecture, and allows the algorithm to allocate attention selectively to both fixated and non-fixated image regions. Through evaluation on the COCO/SALICON datasets we show that our method improves image captioning performance and that gaze can complement machine attention for semantic scene understanding tasks.

Connected through hyperedge HE-1 (induced by source paper "the fluidity of concept representations in human brain signals"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "advancing nlp with cognitive language processing signals"
    - "finding syntax in human encephalography with beam search"
    - "does the brain represent words an evaluation of brain decoding studies of language understanding"
    - "multimodal grounding for language processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "boosted attention leveraging human attention for image captioning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "self critical sequence training for image captioning"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "gaze360 physically unconstrained gaze estimation in the wild"):
  with members:
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "densepose dense human pose estimation in the wild"
    - "adversarial discriminative domain adaptation"
    - "temporal relational reasoning in videos"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"
    - "eye tracking for everyone"
    - "finding tiny faces"

Connected through hyperedge HE-4 (induced by source paper "object referring in videos with language and human gaze"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "end to end representation learning for correlation filter based tracking"
    - "query guided regression network with context policy for phrase grounding"
    - "object referring in visual scene with spoken language"
    - "localizing moments in video with natural language"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "predicting human eye fixations via an lstm based saliency attentive model"):
  with members:
    - "visual saliency prediction using a mixture of deep neural networks"
    - "end to end saliency mapping via probability distribution prediction"
    - "attention is all you need"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "a deep multi level network for saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "paying more attention to saliency image captioning with saliency and context attention"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "gaze embeddings for zero shot image classification"):
  with members:
    - "label embedding for image classification"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "gazedpm early integration of gaze information in deformable part models"
    - "prediction of search targets from fixations in open world settings"
    - "going deeper with convolutions"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-8 (induced by source paper "predicting the category and attributes of visual search targets using deep gaze pooling"):
  with members:
    - "gaze embeddings for zero shot image classification"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "gazedpm early integration of gaze information in deformable part models"
    - "learning deep features for discriminative localization"
    - "prediction of search targets from fixations in open world settings"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)