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: "show adapt and tell adversarial training of cross domain image captioner"
  Abstract: Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5% boost.

Connected through hyperedge HE-1 (induced by source paper "better captioning with sequence level exploration"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "exploring visual relationship for image captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with guidance of multimodal latent topics"
    - "video captioning with transferred semantic attributes"

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"
    - "unsupervised image captioning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-4 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation"):
  with members:
    - "exploration by random network distillation"
    - "diverse and coherent paragraph generation from images"
    - "large scale study of curiosity driven learning"
    - "context aware visual policy network for sequence level image captioning"
    - "coarse to fine annotation enrichment for semantic segmentation learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "improved image captioning via policy gradient optimization of spider"

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"
    - "unsupervised image captioning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "scene based factored attention for image captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "learning to guide decoding for image captioning"
    - "discriminability objective for training descriptive captions"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "locally supervised deep hybrid model for scene recognition"

Connected through hyperedge HE-7 (induced by source paper "canzsl cycle consistent adversarial networks for zero shot learning from natural language"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "cycada cycle consistent adversarial domain adaptation"
    - "camera style adaptation for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "early action prediction with generative adversarial networks"):
  with members:
    - "lattice long short term memory for human action recognition"
    - "visual forecasting by imitating dynamics in natural sequences"
    - "the pose knows video forecasting by generating pose futures"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "real time action recognition with enhanced motion vector cnns"
    - "generative adversarial imitation learning"
    - "semantic image inpainting with deep generative models"
    - "temporal segment networks towards good practices for deep action recognition"

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)