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: "multimodal explanations justifying decisions and pointing to the evidence"
  Abstract: Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.

Connected through hyperedge HE-1 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "explaining with counter visual attributes and examples"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting adversarially trained convolutional neural networks"
    - "counterfactual visual explanations"
    - "grounding visual explanations"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"
    - "textual explanations for self driving vehicles"

Connected through hyperedge HE-3 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-4 (induced by source paper "deep multi modal sets"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "supervised multimodal bitransformers for classifying images and text"
    - "mfas multimodal fusion architecture search"
    - "roberta a robustly optimized bert pretraining approach"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep sets"
    - "exploring the limits of weakly supervised pretraining"
    - "rosetta large scale system for text detection and recognition in images"

Connected through hyperedge HE-5 (induced by source paper "a study on multimodal and interactive explanations for visual question answering"):
  with members:
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "grounding visual explanations"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"

Connected through hyperedge HE-6 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "this looks like that deep learning for interpretable image recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-7 (induced by source paper "explainable deep modeling of tabular data using tablegraphnet"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "squinting at vqa models interrogating vqa models with sub questions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "women also snowboard overcoming bias in captioning models"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "compositional attention networks for machine reasoning"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

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.AI(Artificial Intelligence)