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: "being negative but constructively lessons learnt from creating better visual question answering datasets"
  Abstract: Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (\ie the correct one) and the decoys (\ie the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via this http URL.

Connected through hyperedge HE-1 (induced by source paper "visual question answering on 360 deg images"):
  with members:
    - "towards vqa models that can read"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "an empirical study on leveraging scene graphs for visual question answering"):
  with members:
    - "rethinking visual relationships for high level image understanding"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "vrr vg refocusing visually relevant relationships"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "rubi reducing unimodal biases in visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "right for the right reason training agnostic networks"
    - "explicit bias discovery in visual question answering models"
    - "bilinear attention networks"
    - "from recognition to cognition visual commonsense reasoning"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-4 (induced by source paper "spatialsense an adversarially crowdsourced benchmark for spatial relation recognition"):
  with members:
    - "explicit bias discovery in visual question answering models"
    - "linknet relational embedding for scene graph"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "an analysis of visual question answering algorithms"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-5 (induced by source paper "adversarial regularization for visual question answering strengths shortcomings and side effects"):
  with members:
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "learning to count objects in natural images for visual question answering"
    - "hypothesis only baselines in natural language inference"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-6 (induced by source paper "cross modal and hierarchical modeling of video and text"):
  with members:
    - "jointly localizing and describing events for dense video captioning"
    - "compressed video action recognition"
    - "learning robust visual semantic embeddings"
    - "temporal action detection with structured segment networks"
    - "dense captioning events in videos"
    - "localizing moments in video with natural language"
    - "deep quantization encoding convolutional activations with deep generative model"
    - "real time action recognition with enhanced motion vector cnns"

Connected through hyperedge HE-7 (induced by source paper "cross dataset adaptation for visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "an analysis of visual question answering algorithms"
    - "neural domain adaptation for biomedical question answering"
    - "adversarial discriminative domain adaptation"
    - "survey of visual question answering datasets and techniques"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "the wisdom of masses majority subjectivity and semantic similarity in the evaluation of vqa"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "advances in pre training distributed word representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "embodied question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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.CL(Computation and Language)