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: "sort story sorting jumbled images and captions into stories"
  Abstract: Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication. We propose the task of sequencing -- given a jumbled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms a coherent story. We present multiple approaches, via unary (position) and pairwise (order) predictions, and their ensemble-based combinations, achieving strong results on this task. We use both text-based and image-based features, which depict complementary improvements. Using qualitative examples, we demonstrate that our models have learnt interesting aspects of temporal common sense.

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 "making history matter history advantage sequence training for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "recursive visual attention in visual dialog"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "attention is all you need"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-3 (induced by source paper "deep attentive ranking networks for learning to order sentences"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "context is key new approaches to neural coherence modeling"
    - "an efficient framework for learning sentence representations"
    - "attention is all you need"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural sentence ordering"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-4 (induced by source paper "induction and reference of entities in a visual story"):
  with members:
    - "strategies for structuring story generation"
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "plan and write towards better automatic storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"

Connected through hyperedge HE-5 (induced by source paper "the steep road to happily ever after an analysis of current visual storytelling models"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "story generation from sequence of independent short descriptions"

Connected through hyperedge HE-6 (induced by source paper "generating persuasive visual storylines for promotional videos"):
  with members:
    - "learning video story composition via recurrent neural network"
    - "nima neural image assessment"
    - "building ethics into artificial intelligence"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "learning visual storylines with skipping recurrent neural networks"
    - "will people like your image learning the aesthetic space"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "predicting human activities from user generated content"):
  with members:
    - "smooth loss functions for deep top k classification"
    - "event2mind commonsense inference on events intents and reactions"
    - "direct network transfer transfer learning of sentence embeddings for semantic similarity"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "advances in human action recognition a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "ms marco a human generated machine reading comprehension dataset"

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.CL(Computation and Language)