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: "neural self talk image understanding via continuous questioning and answering"
  Abstract: In this paper we consider the problem of continuously discovering image contents by actively asking image based questions and subsequently answering the questions being asked. The key components include a Visual Question Generation (VQG) module and a Visual Question Answering module, in which Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN) are used. Given a dataset that contains images, questions and their answers, both modules are trained at the same time, with the difference being VQG uses the images as input and the corresponding questions as output, while VQA uses images and questions as input and the corresponding answers as output. We evaluate the self talk process subjectively using Amazon Mechanical Turk, which show effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (induced by source paper "deep bayesian network for visual question generation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "creativity generating diverse questions using variational autoencoders"
    - "what s in a question using visual questions as a form of supervision"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-3 (induced by source paper "deep exemplar networks for vqa and vqg"):
  with members:
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "learning to ask neural question generation for reading comprehension"
    - "creativity generating diverse questions using variational autoencoders"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "hadamard product for low rank bilinear pooling"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-4 (induced by source paper "learning to globally edit images with textual description"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "interactive image manipulation with natural language instruction commands"
    - "cross sentence n ary relation extraction with graph lstms"
    - "language based image editing with recurrent attentive models"
    - "comprehension guided referring expressions"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "i2t2i learning text to image synthesis with textual data augmentation"

Connected through hyperedge HE-5 (induced by source paper "multimodal differential network for visual question generation"):
  with members:
    - "differential attention for visual question answering"
    - "learning to ask neural question generation for reading comprehension"
    - "creativity generating diverse questions using variational autoencoders"
    - "what s in a question using visual questions as a form of supervision"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "matrix neural networks"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "symmetry invariant optimization in deep networks"
    - "creation of a deep convolutional auto encoder in caffe"
    - "tensor principal component analysis via sum of squares proofs"
    - "caffe convolutional architecture for fast feature embedding"

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)