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: "object counts bringing explicit detections back into image captioning"
  Abstract: The use of explicit object detectors as an intermediate step to image captioning - which used to constitute an essential stage in early work - is often bypassed in the currently dominant end-to-end approaches, where the language model is conditioned directly on a mid-level image embedding. We argue that explicit detections provide rich semantic information, and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. We provide an in-depth analysis of end-to-end image captioning by exploring a variety of cues that can be derived from such object detections. Our study reveals that end-to-end image captioning systems rely on matching image representations to generate captions, and that encoding the frequency, size and position of objects are complementary and all play a role in forming a good image representation. It also reveals that different object categories contribute in different ways towards image captioning.

Connected through hyperedge HE-1 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "visuallly grounded generation of entailments from premises"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visual entailment a novel task for fine grained image understanding"
    - "quantifying the amount of visual information used by neural caption generators"
    - "annotation artifacts in natural language inference data"
    - "grounded textual entailment"
    - "where to put the image in an image caption generator"
    - "foil it find one mismatch between image and language caption"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "shapecaptioner generative caption network for 3d shapes by learning a mapping from parts detected in multiple views to sentences"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "turbo learning for captionbot and drawingbot"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "cross modal retrieval with implicit concept association"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"

Connected through hyperedge HE-5 (induced by source paper "going beneath the surface evaluating image captioning for grammaticality truthfulness and diversity"):
  with members:
    - "a call for clarity in reporting bleu scores"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "convolutional image captioning"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-6 (induced by source paper "incorporating textual evidence in visual storytelling"):
  with members:
    - "using deep object features for image descriptions"
    - "contextualize show and tell a neural visual storyteller"
    - "learning to guide decoding for image captioning"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "stories for images in sequence by using visual and narrative components"

Connected through hyperedge HE-7 (induced by source paper "vifidel evaluating the visual fidelity of image descriptions"):
  with members:
    - "end to end image captioning exploits multimodal distributional similarity"
    - "improved image captioning via policy gradient optimization of spider"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "show control and tell a framework for generating controllable and grounded captions"):
  with members:
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "neural baby talk"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "paying more attention to saliency image captioning with saliency and context attention"

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)