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: "context aware visual policy network for sequence level image captioning"
  Abstract: Many vision-language tasks can be reduced to the problem of sequence prediction for natural language output. In particular, recent advances in image captioning use deep reinforcement learning (RL) to alleviate the "exposure bias'' during training: ground-truth subsequence is exposed in every step prediction, which introduces bias in test when only predicted subsequence is seen. However, existing RL-based image captioning methods only focus on the language policy while not the visual policy (eg, visual attention), and thus fail to capture the visual context that are crucial for compositional reasoning such as visual relationships (\eg, "man riding horse'') and comparisons (eg. "smaller cat"). To fill the gap, we propose a Context-Aware Visual Policy network (CAVP) for sequence-level image captioning. At every time step, CAVP explicitly accounts for the previous visual attentions as the context, and then decides whether the context is helpful for the current word generation given the current visual attention. Compared against traditional visual attention that only fixes a single image region at every step, CAVP can attend to complex visual compositions over time. The whole image captioning model --- CAVP and its subsequent language policy network --- can be efficiently optimized end-to-end by using an actor-critic policy gradient method with respect to any caption evaluation metric. We demonstrate the effectiveness of CAVP by state-of-the-art performances on MS-COCO offline split and online server, using various metrics and sensible visualizations of qualitative visual context. The code is available at \urlhttps://github.com/daqingliu/CAVP

Connected through hyperedge HE-1 (induced by source paper "deconfounded image captioning a causal retrospect"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "hierarchy parsing for image captioning"
    - "attention on attention for image captioning"
    - "rubi reducing unimodal biases in visual question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "unified vision language pre training for image captioning and vqa"
    - "context aware visual policy network for fine grained image captioning"

Connected through hyperedge HE-2 (induced by source paper "normalized and geometry aware self attention network for image captioning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale long tailed recognition in an open world"
    - "aligning linguistic words and visual semantic units for image captioning"
    - "context aware self attention networks"

Connected through hyperedge HE-3 (induced by source paper "exploring and distilling cross modal information for image captioning"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "aligning visual regions and textual concepts learning fine grained image representations for image captioning"
    - "learning to guide decoding for image captioning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "image captioning with semantic attention"

Connected through hyperedge HE-4 (induced by source paper "learning to collocate neural modules for image captioning"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "context aware visual policy network for fine grained image captioning"
    - "context and attribute grounded dense captioning"
    - "finding syntax in human encephalography with beam search"
    - "bilinear attention networks"
    - "neural baby talk"
    - "learning to assemble neural module tree networks for visual grounding"

Connected through hyperedge HE-5 (induced by source paper "casenet content adaptive scale interaction networks for scene parsing"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "unified perceptual parsing for scene understanding"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-6 (induced by source paper "pr product a substitute for inner product in neural networks"):
  with members:
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-7 (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"
    - "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"
    - "show adapt and tell adversarial training of cross domain image captioner"

Connected through hyperedge HE-8 (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"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "using artificial tokens to control languages for multilingual image caption generation"

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)