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: "dynamic filtering with large sampling field for convnets"
  Abstract: We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. Such multiple samples enlarge the kernels' receptive fields significantly without requiring more parameters. While LS-DFN inherits the advantages of DFN, namely avoiding feature map blurring by position-wise kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard back-propagation. We demonstrate the merits of our LS-DFN on both sparse and dense prediction tasks involving object detection, semantic segmentation, and flow estimation. Our results show LS-DFN enjoys stronger recognition abilities in object detection and semantic segmentation tasks on VOC benchmark and sharper responses in flow estimation on FlyingChairs dataset compared to strong baselines.

Connected through hyperedge HE-1 (induced by source paper "learning depth guided convolutions for monocular 3d object detection"):
  with members:
    - "disentangling monocular 3d object detection"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning task specific generalized convolutions in the permutohedral lattice"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "differentiable mpc for end to end planning and control"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "generating question relevant captions to aid visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self critical reasoning for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "learning to predict layout to image conditional convolutions for semantic image synthesis"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "meta sr a magnification arbitrary network for super resolution"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"

Connected through hyperedge HE-7 (induced by source paper "pillar in pillar multi scale and dynamic feature extraction for 3d object detection in point clouds"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "learning guided convolutional network for depth completion"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "deep hough voting for 3d object detection in point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "pointpillars fast encoders for object detection from point clouds"
    - "you look twice gaternet for dynamic filter selection in cnns"

Connected through hyperedge HE-8 (induced by source paper "faithful multimodal explanation for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "self critical reasoning for robust visual question answering"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "constituency parsing with a self attentive encoder"
    - "joint image captioning and question answering"
    - "network dissection quantifying interpretability of deep visual representations"

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.CV(Computer Vision and Pattern Recognition)