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: "survey of visual question answering datasets and techniques"
  Abstract: Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of the survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.

Connected through hyperedge HE-1 (induced by source paper "learning by abstraction the neural state machine"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "measuring compositionality in representation learning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answer them all toward universal visual question answering models"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"

Connected through hyperedge HE-2 (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"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "learning answer embeddings for visual question answering"):
  with members:
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "a simple loss function for improving the convergence and accuracy of visual question answering models"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "compositional attention networks for machine reasoning"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "film visual reasoning with a general conditioning layer"
    - "analyzing the behavior of visual question answering models"
    - "key value memory networks for directly reading documents"
    - "learning to compose neural networks for question answering"
    - "towards deep symbolic reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "zero shot transfer vqa dataset"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering datasets algorithms and future challenges"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "learning to caption images through a lifetime by asking questions"):
  with members:
    - "visual question generation for class acquisition of unknown objects"
    - "visual reasoning by progressive module networks"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "visual question generation as dual task of visual question answering"
    - "learning by asking questions"
    - "teaching machines to describe images via natural language feedback"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-7 (induced by source paper "on the flip side identifying counterexamples in visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question answering a survey of methods and datasets"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "visual7w grounded question answering in images"
    - "vqa visual question answering"
    - "skip thought vectors"
    - "deep residual learning for image recognition"

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)