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: "exploring human like attention supervision in visual question answering"
  Abstract: Attention mechanisms have been widely applied in the Visual Question Answering (VQA) task, as they help to focus on the area-of-interest of both visual and textual information. To answer the questions correctly, the model needs to selectively target different areas of an image, which suggests that an attention-based model may benefit from an explicit attention supervision. In this work, we aim to address the problem of adding attention supervision to VQA models. Since there is a lack of human attention data, we first propose a Human Attention Network (HAN) to generate human-like attention maps, training on a recently released dataset called Human ATtention Dataset (VQA-HAT). Then, we apply the pre-trained HAN on the VQA v2.0 dataset to automatically produce the human-like attention maps for all image-question pairs. The generated human-like attention map dataset for the VQA v2.0 dataset is named as Human-Like ATtention (HLAT) dataset. Finally, we apply human-like attention supervision to an attention-based VQA model. The experiments show that adding human-like supervision yields a more accurate attention together with a better performance, showing a promising future for human-like attention supervision in VQA.

Connected through hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "learning by abstraction the neural state machine"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "simple but effective techniques to reduce biases"

Connected through hyperedge HE-2 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "cbam convolutional block attention module"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "clothing retrieval with visual attention model"
    - "attention is all you need"
    - "automatic spatially aware fashion concept discovery"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-3 (induced by source paper "self critical reasoning for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generating question relevant captions to aid visual question answering"
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "attention is not explanation"
    - "visual entailment a novel task for fine grained image understanding"
    - "cycle consistency for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "bilinear attention networks"

Connected through hyperedge HE-4 (induced by source paper "mirrorgan learning text to image generation by redescription"):
  with members:
    - "ancient painting to natural image a new solution for painting processing"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generative image inpainting with contextual attention"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-5 (induced by source paper "adversarial tableqa attention supervision for question answering on tables"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "question answering through transfer learning from large fine grained supervision data"
    - "stochastic answer networks for machine reading comprehension"
    - "weakly supervised semantic parsing with abstract examples"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "supervising neural attention models for video captioning by human gaze data"
    - "adversarial examples for evaluating reading comprehension systems"
    - "language to logical form with neural attention"

Connected through hyperedge HE-6 (induced by source paper "interpretable visual question answering by visual grounding from attention supervision mining"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "hierarchical question image co attention for visual question answering"
    - "visual question answering a survey of methods and datasets"

Connected through hyperedge HE-7 (induced by source paper "learning compositionally through attentive guidance"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "compositional attention networks for machine reasoning"
    - "the fine line between linguistic generalization and failure in seq2seq attention models"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "making neural programming architectures generalize via recursion"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "modular multitask reinforcement learning with policy sketches"

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)