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: "obj2text generating visually descriptive language from object layouts"
  Abstract: Generating captions for images is a task that has recently received considerable attention. In this work we focus on caption generation for abstract scenes, or object layouts where the only information provided is a set of objects and their locations. We propose OBJ2TEXT, a sequence-to-sequence model that encodes a set of objects and their locations as an input sequence using an LSTM network, and decodes this representation using an LSTM language model. We show that our model, despite encoding object layouts as a sequence, can represent spatial relationships between objects, and generate descriptions that are globally coherent and semantically relevant. We test our approach in a task of object-layout captioning by using only object annotations as inputs. We additionally show that our model, combined with a state-of-the-art object detector, improves an image captioning model from 0.863 to 0.950 (CIDEr score) in the test benchmark of the standard MS-COCO Captioning task.

Connected through hyperedge HE-1 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "video question generation via cross modal self attention networks learning"):
  with members:
    - "lifelong learning for image captioning by asking natural language questions"
    - "end to end dense video captioning with masked transformer"
    - "tvqa localized compositional video question answering"
    - "from film to video multi turn question answering with multi modal context"
    - "learning to ask neural question generation for reading comprehension"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "gaining extra supervision via multi task learning for multi modal video question answering"):
  with members:
    - "a joint sequence fusion model for video question answering and retrieval"
    - "multimodal dual attention memory for video story question answering"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (induced by source paper "referring to objects in videos using spatio temporal identifying descriptions"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "tvqa localized compositional video question answering"
    - "actor and action video segmentation from a sentence"
    - "flow guided feature aggregation for video object detection"
    - "dense captioning events in videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-5 (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-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"
    - "object counts bringing explicit detections back into image captioning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"

Connected through hyperedge HE-7 (induced by source paper "tvqa localized compositional video question answering"):
  with members:
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"
    - "deepstory video story qa by deep embedded memory networks"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-8 (induced by source paper "text2scene generating compositional scenes from textual descriptions"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "imagine this scripts to compositions to videos"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi parametric image synthesis"
    - "image generation from scene graphs"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "photographic image synthesis with cascaded refinement networks"

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)