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 counting for visual question answering"
  Abstract: Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimated from each section of the image. In contrast, we treat counting as a sequential decision process and force our model to make discrete choices of what to count. Specifically, the model sequentially selects from detected objects and learns interactions between objects that influence subsequent selections. A distinction of our approach is its intuitive and interpretable output, as discrete counts are automatically grounded in the image. Furthermore, our method outperforms the state of the art architecture for VQA on multiple metrics that evaluate counting.

Connected through hyperedge HE-1 (induced by source paper "in defense of grid features for visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "visualbert a simple and performant baseline for vision and language"
    - "unified vision language pre training for image captioning and vqa"
    - "answer them all toward universal visual question answering models"

Connected through hyperedge HE-2 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "on variational bounds of mutual information"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-3 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning to reason with relational video representation for question answering"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "progressive attention memory network for movie story question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-6 (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-7 (induced by source paper "inverse visual question answering a new benchmark and vqa diagnosis tool"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "ivqa inverse visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "the promise of premise harnessing question premises in visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "active learning for visual question answering an empirical study"

Connected through hyperedge HE-8 (induced by source paper "interpretable visual question answering by reasoning on dependency trees"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning to count objects in natural images for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "a simple neural network module for relational 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.AI(Artificial Intelligence)