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: "stacked attention networks for image question answering"
  Abstract: This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-2 (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"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "explainable reinforcement learning through a causal lens"
    - "learning interpretable models with causal guarantees"
    - "explaining classifiers with causal concept effect cace"
    - "neural network attributions a causal perspective"
    - "on the accuracy of influence functions for measuring group effects"
    - "explaining deep learning models with constrained adversarial examples"

Connected through hyperedge HE-4 (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-5 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "component analysis for visual question answering architectures"):
  with members:
    - "m 2 meshed memory transformer for image captioning"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "language features matter effective language representations for vision language tasks"
    - "evalai towards better evaluation systems for ai agents"
    - "murel multimodal relational reasoning for visual question answering"
    - "deep learning using rectified linear units relu"
    - "stacked cross attention for image text matching"
    - "generalized hadamard product fusion operators for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "visual question answering on 360 deg images"):
  with members:
    - "towards vqa models that can read"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bert pre training of deep bidirectional transformers for language understanding"

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.LG(Machine Learning)