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: "towards a visual turing challenge"
  Abstract: As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.

Connected through hyperedge HE-1 (induced by source paper "assessing the robustness of visual question answering"):
  with members:
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "robustness via curvature regularization and vice versa"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "hadamard product for low rank bilinear pooling"
    - "ask your neurons a deep learning approach to visual question answering"

Connected through hyperedge HE-2 (induced by source paper "compact trilinear interaction for visual question answering"):
  with members:
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-3 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

Connected through hyperedge HE-4 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-5 (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-6 (induced by source paper "object based reasoning in vqa"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "inferring and executing programs for visual reasoning"
    - "neural collaborative filtering"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "detecting visual relationships with deep relational networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "weakly supervised visual grounding of phrases with linguistic structures"

Connected through hyperedge HE-7 (induced by source paper "learning visual knowledge memory networks for visual question answering"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "cascade r cnn delving into high quality object detection"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "structured attentions for visual question answering"
    - "weakly supervised dense video captioning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "attentive pooling networks"

Connected through hyperedge HE-8 (induced by source paper "answering visual what if questions from actions to predicted scene descriptions"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "dvqa understanding data visualizations via question answering"
    - "visual interaction networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "taking visual motion prediction to new heightfields"

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)