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: "zero shot visual question answering"
  Abstract: Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that illustrate every possible concept, and will therefore never achieve this capability, since the volume of required training data would be prohibitive. Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions. We propose a new evaluation protocol for VQA methods which measures their ability to perform Zero-Shot VQA, and in doing so highlights significant practical deficiencies of current approaches, some of which are masked by the biases in current datasets. We propose and evaluate several strategies for achieving Zero-Shot VQA, including methods based on pretrained word embeddings, object classifiers with semantic embeddings, and test-time retrieval of example images. Our extensive experiments are intended to serve as baselines for Zero-Shot VQA, and they also achieve state-of-the-art performance in the standard VQA evaluation setting.

Connected through hyperedge HE-1 (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-2 (induced by source paper "v prom a benchmark for visual reasoning using visual progressive matrices"):
  with members:
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"
    - "learning generalized reactive policies using deep neural networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "compact trilinear interaction for visual question answering"):
  with members:
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-4 (induced by source paper "non monotonic logical reasoning guiding deep learning for explainable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning conditioned graph structures for interpretable visual question answering"

Connected through hyperedge HE-5 (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"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "from known to the unknown transferring knowledge to answer questions about novel visual and semantic concepts"):
  with members:
    - "reciprocal attention fusion for visual question answering"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a zero shot framework for sketch based image retrieval"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "a simple exponential family framework for zero shot learning"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "zero shot learning the good the bad and the ugly"
    - "fast zero shot image tagging"

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)