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: "ask me anything free form visual question answering based on knowledge from external sources"
  Abstract: We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported results in both cases.

Connected through hyperedge HE-1 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "understanding chat messages for sticker recommendation in hike messenger"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "product aware answer generation in e commerce question answering"
    - "how to write summaries with patterns learning towards abstractive summarization through prototype editing"
    - "abstractive text summarization by incorporating reader comments"
    - "think visually question answering through virtual imagery"
    - "multimodal emoji prediction"

Connected through hyperedge HE-2 (induced by source paper "on the general value of evidence and bilingual scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "chinese street view text large scale chinese text reading with partially supervised learning"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "visual question answering datasets algorithms and future challenges"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-4 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "learning visual knowledge memory networks for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "ok vqa a visual question answering benchmark requiring external knowledge"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an implementation of faster rcnn with study for region sampling"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"

Connected through hyperedge HE-6 (induced by source paper "assessing the robustness of visual question answering"):
  with members:
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "robustness via curvature regularization and vice versa"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "hadamard product for low rank bilinear pooling"
    - "ask your neurons a deep learning approach to visual question answering"

Connected through hyperedge HE-7 (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-8 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "deep nets what have they ever done for vision"
    - "natural language to structured query generation via meta learning"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"

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)