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: "where to put the image in an image caption generator"
  Abstract: When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN -- conditioning the language model by `injecting' image features -- or in a layer following the RNN -- conditioning the language model by `merging' image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper we empirically show that it is not especially detrimental to performance whether one architecture is used or another. The merge architecture does have practical advantages, as conditioning by merging allows the RNN's hidden state vector to shrink in size by up to four times. Our results suggest that the visual and linguistic modalities for caption generation need not be jointly encoded by the RNN as that yields large, memory-intensive models with few tangible advantages in performance; rather, the multimodal integration should be delayed to a subsequent stage.

Connected through hyperedge HE-1 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-3 (induced by source paper "visuallly grounded generation of entailments from premises"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visual entailment a novel task for fine grained image understanding"
    - "quantifying the amount of visual information used by neural caption generators"
    - "annotation artifacts in natural language inference data"
    - "object counts bringing explicit detections back into image captioning"
    - "grounded textual entailment"
    - "foil it find one mismatch between image and language caption"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"

Connected through hyperedge HE-4 (induced by source paper "transfer learning from language models to image caption generators better models may not transfer better"):
  with members:
    - "universal language model fine tuning for text classification"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "unsupervised pretraining for sequence to sequence learning"
    - "how transferable are neural networks in nlp applications"
    - "transfer learning for low resource neural machine translation"
    - "language models for image captioning the quirks and what works"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

Connected through hyperedge HE-5 (induced by source paper "attend more times for image captioning"):
  with members:
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "exploring visual relationship for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "self critical sequence training for image captioning"
    - "deep residual learning for image recognition"
    - "stacked attention networks for image question answering"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "face2text collecting an annotated image description corpus for the generation of rich face descriptions"):
  with members:
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "multi30k multilingual english german image descriptions"
    - "self critical sequence training for image captioning"
    - "language models for image captioning the quirks and what works"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "deep learning face attributes in the wild"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-7 (induced by source paper "pre gen metrics predicting caption quality metrics without generating captions"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "cider consensus based image description evaluation"
    - "microsoft coco common objects in context"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "quantifying the amount of visual information used by neural caption generators"):
  with members:
    - "foil it find one mismatch between image and language caption"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "representation of linguistic form and function in recurrent neural networks"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"

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.NE(Neural and Evolutionary Computing)