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: "vqa e explaining elaborating and enhancing your answers for visual questions"
  Abstract: Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanation for an answer is of the same or even more importance compared with the answer itself, since it makes the question and answering process more understandable and traceable. To this end, we propose a new task of VQA-E (VQA with Explanation), where the computational models are required to generate an explanation with the predicted answer. We first construct a new dataset, and then frame the VQA-E problem in a multi-task learning architecture. Our VQA-E dataset is automatically derived from the VQA v2 dataset by intelligently exploiting the available captions. We have conducted a user study to validate the quality of explanations synthesized by our method. We quantitatively show that the additional supervision from explanations can not only produce insightful textual sentences to justify the answers, but also improve the performance of answer prediction. Our model outperforms the state-of-the-art methods by a clear margin on the VQA v2 dataset.

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 "generating question relevant captions to aid visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self critical reasoning for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "dynamic filtering with large sampling field for convnets"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "why do these match explaining the behavior of image similarity models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "discriminative learning of latent features for zero shot recognition"
    - "a zero shot framework for sketch based image retrieval"
    - "textual explanations for self driving vehicles"

Connected through hyperedge HE-4 (induced by source paper "gqa a new dataset for real world visual reasoning and compositional question answering"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "compositional attention networks for machine reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "scene graph generation by iterative message passing"
    - "the promise of premise harnessing question premises in visual question answering"

Connected through hyperedge HE-5 (induced by source paper "shuffle then assemble learning object agnostic visual relationship features"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "context aware visual policy network for sequence level image captioning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "vip cnn visual phrase guided convolutional neural network"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-6 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "object based reasoning in vqa"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-8 (induced by source paper "joint image captioning and question answering"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "attention on attention architectures for visual question answering vqa"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "diverse beam search decoding diverse solutions from neural sequence models"

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)