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: "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
  Abstract: Visual Question Answering (VQA) has received a lot of attention over the past couple of years. A number of deep learning models have been proposed for this task. However, it has been shown that these models are heavily driven by superficial correlations in the training data and lack compositionality -- the ability to answer questions about unseen compositions of seen concepts. This compositionality is desirable and central to intelligence. In this paper, we propose a new setting for Visual Question Answering where the test question-answer pairs are compositionally novel compared to training question-answer pairs. To facilitate developing models under this setting, we present a new compositional split of the VQA v1.0 dataset, which we call Compositional VQA (C-VQA). We analyze the distribution of questions and answers in the C-VQA splits. Finally, we evaluate several existing VQA models under this new setting and show that the performances of these models degrade by a significant amount compared to the original VQA setting.

Connected through hyperedge HE-1 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "do explanations make vqa models more predictable to a human"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "closure assessing systematic generalization of clevr models"):
  with members:
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "rearranging the familiar testing compositional generalization in recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "sunny and dark outside improving answer consistency in vqa through entailed question generation"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "evaluating quality of chatbots and intelligent conversational agents"
    - "learning by asking questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "fooling vision and language models despite localization and attention mechanism"

Connected through hyperedge HE-6 (induced by source paper "on controlled deentanglement for natural language processing"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-7 (induced by source paper "a corpus for reasoning about natural language grounded in photographs"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "visual entailment a novel task for fine grained image understanding"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "using syntax to ground referring expressions in natural images"
    - "learning visual question answering by bootstrapping hard attention"
    - "talk the walk navigating new york city through grounded dialogue"

Connected through hyperedge HE-8 (induced by source paper "transfer learning via unsupervised task discovery for visual question answering"):
  with members:
    - "neural baby talk"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "dvqa understanding data visualizations via question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "extreme clicking for efficient object annotation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "aligned image word representations improve inductive transfer across vision language tasks"

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)