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: "interpretable visual question answering by visual grounding from attention supervision mining"
  Abstract: A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that our model trained with this mined supervision generates visual groundings that achieve a higher correlation with respect to manually-annotated groundings, meanwhile achieving state-of-the-art VQA accuracy.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning to generate grounded image captions without localization supervision"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "cycle consistency for robust visual question answering"
    - "grounding visual explanations"
    - "turbo learning for captionbot and drawingbot"
    - "neural baby talk"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "weakly supervised dense event captioning in videos"
    - "grounded video description"

Connected through hyperedge HE-4 (induced by source paper "zero shot grounding of objects from natural language queries"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "neural baby talk"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-5 (induced by source paper "revisiting embodiedqa a simple baseline and beyond"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "multi target embodied question answering"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "blindfold baselines for embodied qa"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "a better way to attend attention with trees for video question answering"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-6 (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-7 (induced by source paper "grounded video description"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "move forward and tell a progressive generator of video descriptions"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"

Connected through hyperedge HE-8 (induced by source paper "show control and tell a framework for generating controllable and grounded captions"):
  with members:
    - "neural baby talk"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "object counts bringing explicit detections back into image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "paying more attention to saliency image captioning with saliency and context attention"

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)