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: "a survey of current datasets for vision and language research"
  Abstract: Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The available corpora have played a crucial role in advancing this area of research. In this paper, we propose a set of quality metrics for evaluating and analyzing the vision & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been using more complex language and more abstract concepts, however, there are different strengths and weaknesses in each.

Connected through hyperedge HE-1 (induced by source paper "evaluating text to image matching using binary image selection bison"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "stacked cross attention for image text matching"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "exploring the limits of weakly supervised pretraining"
    - "engaging image captioning via personality"
    - "learning to ask neural question generation for reading comprehension"
    - "context aware captions from context agnostic supervision"
    - "convolutional image captioning"

Connected through hyperedge HE-2 (induced by source paper "visual understanding and narration a deeper understanding and explanation of visual scenes"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "a pipeline for creative visual storytelling"
    - "temporal event knowledge acquisition via identifying narratives"
    - "scoutbot a dialogue system for collaborative navigation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "dock detecting objects by transferring common sense knowledge"
    - "hierarchical neural story generation"
    - "hierarchically attentive rnn for album summarization and storytelling"

Connected through hyperedge HE-3 (induced by source paper "cross dataset adaptation for visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"
    - "neural domain adaptation for biomedical question answering"
    - "adversarial discriminative domain adaptation"
    - "survey of visual question answering datasets and techniques"

Connected through hyperedge HE-4 (induced by source paper "survey of the state of the art in natural language generation core tasks applications and evaluation"):
  with members:
    - "toward controlled generation of text"
    - "controlling linguistic style aspects in neural language generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "affective neural response generation"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a persona based neural conversation model"
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "query focused opinion summarization for user generated content"

Connected through hyperedge HE-5 (induced by source paper "tgif qa toward spatio temporal reasoning in visual question answering"):
  with members:
    - "image captioning with semantic attention"
    - "multimodal residual learning for visual qa"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "tgif a new dataset and benchmark on animated gif description"
    - "video2gif automatic generation of animated gifs from video"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-6 (induced by source paper "e qraq a multi turn reasoning dataset and simulator with explanations"):
  with members:
    - "graying the black box understanding dqns"
    - "not just a black box learning important features through propagating activation differences"
    - "dialog based language learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "deep residual learning for image recognition"
    - "language understanding for text based games using deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "an analysis of action recognition datasets for language and vision tasks"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual relationship detection with language priors"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "describing common human visual actions in images"
    - "vqa visual question answering"
    - "visual semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "youtube 8m a large scale video classification benchmark"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "microsoft coco common objects in context"

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)