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: "compositional attention networks for machine reasoning"
  Abstract: We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility. The model approaches problems by decomposing them into a series of attention-based reasoning steps, each performed by a novel recurrent Memory, Attention, and Composition (MAC) cell that maintains a separation between control and memory. By stringing the cells together and imposing structural constraints that regulate their interaction, MAC effectively learns to perform iterative reasoning processes that are directly inferred from the data in an end-to-end approach. We demonstrate the model's strength, robustness and interpretability on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the model is computationally-efficient and data-efficient, in particular requiring 5x less data than existing models to achieve strong results.

Connected through hyperedge HE-1 (induced by source paper "unshuffling data for improved generalization"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "quantifying and alleviating the language prior problem in visual question answering"
    - "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"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "giving commands to a self driving car a multimodal reasoner for visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "talk2car taking control of your self driving car"
    - "objects as points"
    - "mattnet modular attention network for referring expression comprehension"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

Connected through hyperedge HE-3 (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"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "women also snowboard overcoming bias in captioning models"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-4 (induced by source paper "visual concept metaconcept learning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-6 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "do explanations make vqa models more predictable to a human"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-8 (induced by source paper "visual entailment a novel task for fine grained image understanding"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bilinear attention networks"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "visual reasoning by progressive module networks"
    - "grounded textual entailment"
    - "attention on attention architectures for visual question answering vqa"

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)