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: "unsupervised image captioning"
  Abstract: Deep neural networks have achieved great successes on the image captioning task. However, most of the existing models depend heavily on paired image-sentence datasets, which are very expensive to acquire. In this paper, we make the first attempt to train an image captioning model in an unsupervised manner. Instead of relying on manually labeled image-sentence pairs, our proposed model merely requires an image set, a sentence corpus, and an existing visual concept detector. The sentence corpus is used to teach the captioning model how to generate plausible sentences. Meanwhile, the knowledge in the visual concept detector is distilled into the captioning model to guide the model to recognize the visual concepts in an image. In order to further encourage the generated captions to be semantically consistent with the image, the image and caption are projected into a common latent space so that they can reconstruct each other. Given that the existing sentence corpora are mainly designed for linguistic research and are thus with little reference to image contents, we crawl a large-scale image description corpus of two million natural sentences to facilitate the unsupervised image captioning scenario. Experimental results show that our proposed model is able to produce quite promising results without any caption annotations.

Connected through hyperedge HE-1 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised image captioning with shared multimodal embeddings"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (induced by source paper "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "neural sequential phrase grounding seqground"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "yolov3 an incremental improvement"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-5 (induced by source paper "image captioning with very scarce supervised data adversarial semi supervised learning approach"):
  with members:
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "textual explanations for self driving vehicles"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "iterative learning with open set noisy labels"
    - "joint training for neural machine translation models with monolingual data"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-6 (induced by source paper "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "unpaired image captioning by language pivoting"
    - "engaging image captioning via personality"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-7 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unpaired image captioning by language pivoting"
    - "using artificial tokens to control languages for multilingual image caption generation"

Connected through hyperedge HE-8 (induced by source paper "learning cross modal context graph for visual grounding"):
  with members:
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "neural sequential phrase grounding seqground"
    - "language conditioned graph networks for relational reasoning"
    - "murel multimodal relational reasoning for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"

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)