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: "ask your neurons a deep learning approach to visual question answering"
  Abstract: We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. #R##N#In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. #R##N#Moreover, we also extend our analysis to VQA, a large-scale question answering about images dataset, where we investigate some particular design choices and show the importance of stronger visual models. At the same time, we achieve strong performance of our model that still uses a global image representation. Finally, based on such analysis, we refine our Ask Your Neurons on DAQUAR, which also leads to a better performance on this challenging task.

Connected through hyperedge HE-1 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning to follow directions in street view"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "unsupervised multi modal neural machine translation"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-4 (induced by source paper "ai powered text generation for harmonious human machine interaction current state and future directions"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "learning personalized end to end goal oriented dialog"
    - "multi reward reinforced summarization with saliency and entailment"
    - "learning to generate reviews and discovering sentiment"
    - "key value retrieval networks for task oriented dialogue"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "automatic generation of natural language explanations"

Connected through hyperedge HE-5 (induced by source paper "assessing the robustness of visual question answering"):
  with members:
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "robustness via curvature regularization and vice versa"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "hadamard product for low rank bilinear pooling"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-6 (induced by source paper "chip channel wise disentangled interpretation of deep convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "libra r cnn towards balanced learning for object detection"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "mask r cnn"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "the mythos of model interpretability"

Connected through hyperedge HE-7 (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-8 (induced by source paper "auglabel exploiting word representations to augment labels for face attribute classification"):
  with members:
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "improving facial attribute prediction using semantic segmentation"
    - "regularizing neural networks by penalizing confident output distributions"
    - "disturblabel regularizing cnn on the loss layer"
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "generative adversarial text to image synthesis"

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)