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: "image caption generation with text conditional semantic attention"
  Abstract: We propose a semantic attention mechanism for image caption generation, called text-conditional semantic attention, which allows the caption generator to automatically learn which parts of the image feature to focus on given previously generated text. To acquire text-related image features for our attention model, we also improve the guiding Long Short-Term Memory (gLSTM) structure by back-propagating the training loss though semantic guidance to fine-tune the CNN weights. In contrast to existing gLSTM methods, such as emb-gLSTM, our fine-tuned model enables guidance information to be more text-related. This also allows jointly learning of the image embedding, text embedding, semantic attention and language model with one simple network architecture in an end-to-end manner. We implement our model based on NeuralTalk2, an open-source image caption generator, and test it on MSCOCO dataset. We evaluate the proposed method with three metrics: BLEU, METEOR and CIDEr. The proposed methods outperform state-of-the-art methods.

Connected through hyperedge HE-1 (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-2 (induced by source paper "improving image captioning by leveraging knowledge graphs"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "yolo9000 better faster stronger"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "boosting image captioning with attributes"
    - "what value do explicit high level concepts have in vision to language problems"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-3 (induced by source paper "where to put the image in an image caption generator"):
  with members:
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "boosting image captioning with attributes"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "theano a python framework for fast computation of mathematical expressions"
    - "language models for image captioning the quirks and what works"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "image captioning based on deep reinforcement learning"):
  with members:
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "boosting image captioning with attributes"
    - "sequence level training with recurrent neural networks"
    - "guiding long short term memory for image caption generation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "deep captioning with multimodal recurrent neural networks m rnn"

Connected through hyperedge HE-5 (induced by source paper "ivqa inverse visual question answering"):
  with members:
    - "image grounded conversations multimodal context for natural question and response generation"
    - "hadamard product for low rank bilinear pooling"
    - "analyzing the behavior of visual question answering models"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "semantic regularisation for recurrent image annotation"
    - "generating natural questions about an image"
    - "automatic generation of grounded visual questions"
    - "fvqa fact based visual question answering"

Connected through hyperedge HE-6 (induced by source paper "towards diverse and natural image descriptions via a conditional gan"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation from objects phrases and region captions"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-7 (induced by source paper "what is the role of recurrent neural networks rnns in an image caption generator"):
  with members:
    - "where to put the image in an image caption generator"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "unsupervised pretraining for sequence to sequence learning"
    - "encode review and decode reviewer module for caption generation"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep compositional captioning describing novel object categories without paired training data"

Connected through hyperedge HE-8 (induced by source paper "boosting image captioning with attributes"):
  with members:
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "video captioning with transferred semantic attributes"
    - "image captioning with semantic attention"
    - "rich image captioning in the wild"
    - "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"
    - "watch what you just said image captioning with text conditional attention"

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)