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: "visual question answering a survey of methods and datasets"
  Abstract: Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.

Connected through hyperedge HE-1 (induced by source paper "evaluating the progress of deep learning for visual relational concepts"):
  with members:
    - "v prom a benchmark for visual reasoning using visual progressive matrices"
    - "same different problems strain convolutional neural networks"
    - "a simple neural network module for relational reasoning"
    - "squeeze and excitation networks"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "iq of neural networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-2 (induced by source paper "rsvqa visual question answering for remote sensing data"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "answer them all toward universal visual question answering models"
    - "learning to count objects in natural images for visual question answering"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-3 (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-4 (induced by source paper "visual entailment a novel task for fine grained image understanding"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bilinear attention networks"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "visual reasoning by progressive module networks"
    - "grounded textual entailment"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-5 (induced by source paper "an empirical study on leveraging scene graphs for visual question answering"):
  with members:
    - "rethinking visual relationships for high level image understanding"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "vrr vg refocusing visually relevant relationships"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "answer them all toward universal visual question answering models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "vqd visual query detection in natural scenes"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "reciprocal attention fusion for visual question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visual question answering by bootstrapping hard attention"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "boolq exploring the surprising difficulty of natural yes no questions"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transforming question answering datasets into natural language inference datasets"
    - "interpretation of natural language rules in conversational machine reading"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

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)