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: "contrastive learning for image captioning"
  Abstract: Image captioning, a popular topic in computer vision, has achieved substantial progress in recent years. However, the distinctiveness of natural descriptions is often overlooked in previous work. It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects. In this work, we propose a new learning method, Contrastive Learning (CL), for image captioning. Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the overall quality of the generated captions. We tested our method on two challenging datasets, where it improves the baseline model by significant margins. We also showed in our studies that the proposed method is generic and can be used for models with various structures.

Connected through hyperedge HE-1 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "which encoding is the best for text classification in chinese english japanese and korean"

Connected through hyperedge HE-2 (induced by source paper "dense relational captioning triple stream networks for relationship based captioning"):
  with members:
    - "linknet relational embedding for scene graph"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "inferring and executing programs for visual reasoning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "deep metric learning beyond binary supervision"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "siamese instance search for tracking"
    - "spice semantic propositional image caption evaluation"
    - "deep metric learning via facility location"

Connected through hyperedge HE-4 (induced by source paper "contrastively smoothed class alignment for unsupervised domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "learning to adapt structured output space for semantic segmentation"
    - "co regularized alignment for unsupervised domain adaptation"
    - "multi adversarial domain adaptation"
    - "adaptive semi supervised learning for cross domain sentiment classification"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "triple generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "learning perceptual inference by contrasting"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "raven a dataset for relational and analogical visual reasoning"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "iq of neural networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-6 (induced by source paper "discriminability objective for training descriptive captions"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "learning two branch neural networks for image text matching tasks"
    - "actor critic sequence training for image captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "context aware captions from context agnostic supervision"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-7 (induced by source paper "semantic bottleneck for computer vision tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpreting cnns via decision trees"
    - "explanation methods in deep learning users values concerns and challenges"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "a survey of methods for explaining black box models"

Connected through hyperedge HE-8 (induced by source paper "show tell and discriminate image captioning by self retrieval with partially labeled data"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "context aware captions from context agnostic supervision"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "stack captioning coarse to fine learning for image captioning"
    - "vse improving visual semantic embeddings with hard negatives"

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)