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: "the amazing mysteries of the gutter drawing inferences between panels in comic book narratives"
  Abstract: Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the "gutters" between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called "closure". While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We construct a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language.

Connected through hyperedge HE-1 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents"):
  with members:
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "exploring the limits of weakly supervised pretraining"
    - "learning transferable architectures for scalable image recognition"
    - "weakly supervised learning of visual relations"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-2 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "cnn based extraction of panels characters from bengali comic book page images"):
  with members:
    - "yolov3 an incremental improvement"
    - "sketch based manga retrieval using manga109 dataset"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep cnn based speech balloon detection and segmentation for comic books"):
  with members:
    - "object detection for comics using manga109 annotations"
    - "sketch based manga retrieval using manga109 dataset"
    - "mask r cnn"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "simulating action dynamics with neural process networks"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "a diagram is worth a dozen images"
    - "squad 100 000 questions for machine comprehension of text"
    - "sort story sorting jumbled images and captions into stories"

Connected through hyperedge HE-6 (induced by source paper "a survey of comics research in computer science"):
  with members:
    - "using posters to recommend anime and mangas in a cold start scenario"
    - "comicolorization semi automatic manga colorization"
    - "sketch based manga retrieval using manga109 dataset"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "obamanet photo realistic lip sync from text"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer for anime sketches with enhanced residual u net and auxiliary classifier gan"

Connected through hyperedge HE-7 (induced by source paper "learning to color from language"):
  with members:
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "film visual reasoning with a general conditioning layer"
    - "learning representations for automatic colorization"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning what and where to draw"
    - "convolutional two stream network fusion for video action recognition"
    - "colorful image colorization"

Connected through hyperedge HE-8 (induced by source paper "object detection for comics using manga109 annotations"):
  with members:
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "sketch based manga retrieval using manga109 dataset"
    - "chainercv a library for deep learning in computer vision"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"
    - "faster r cnn towards real time object detection with region proposal 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)