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: "learning to evaluate image captioning"
  Abstract: Evaluation metrics for image captioning face two challenges. Firstly, commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not correlate well with human judgments. Secondly, each metric has well known blind spots to pathological caption constructions, and rule-based metrics lack provisions to repair such blind spots once identified. For example, the newly proposed SPICE correlates well with human judgments, but fails to capture the syntactic structure of a sentence. To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions. In addition, we further propose a data augmentation scheme to explicitly incorporate pathological transformations as negative examples during training. The proposed metric is evaluated with three kinds of robustness tests and its correlation with human judgments. Extensive experiments show that the proposed data augmentation scheme not only makes our metric more robust toward several pathological transformations, but also improves its correlation with human judgments. Our metric outperforms other metrics on both caption level human correlation in Flickr 8k and system level human correlation in COCO. The proposed approach could be served as a learning based evaluation metric that is complementary to existing rule-based metrics.

Connected through hyperedge HE-1 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-2 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-3 (induced by source paper "good news everyone context driven entity aware captioning for news images"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "entity aware image caption generation"
    - "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"
    - "rich image captioning in the wild"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-5 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "better rewards yield better summaries learning to summarise without references"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-6 (induced by source paper "describing like humans on diversity in image captioning"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "gated hierarchical attention for image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-7 (induced by source paper "transfer reward learning for policy gradient based text generation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "attention correctness in neural image captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an actor critic algorithm for sequence prediction"
    - "reward augmented maximum likelihood for neural structured prediction"

Connected through hyperedge HE-8 (induced by source paper "tiger text to image grounding for image caption evaluation"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "stacked cross attention for image text matching"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "neural approaches to conversational ai"
    - "learning two branch neural networks for image text matching tasks"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "towards diverse and natural image descriptions via a conditional gan"

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)