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: "do semantic parts emerge in convolutional neural networks"
  Abstract: Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network.

Connected through hyperedge HE-1 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "minneapple a benchmark dataset for apple detection and segmentation"
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a novel method for the extrinsic calibration of a 2d laser rangefinder and a camera"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "network dissection quantifying interpretability of deep visual representations"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-2 (induced by source paper "semantic hierarchy emerges in the deep generative representations for scene synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "singan learning a generative model from a single natural image"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "semantic image synthesis with spatially adaptive normalization"
    - "interpreting the latent space of gans for semantic face editing"
    - "3d aware scene manipulation via inverse graphics"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-3 (induced by source paper "scops self supervised co part segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "deep feature factorization for concept discovery"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "network dissection quantifying interpretability of deep visual representations"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "manifestation of image contrast in deep networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "how is contrast encoded in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-5 (induced by source paper "semantically interpretable activation maps what where how explanations within cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradmask reduce overfitting by regularizing saliency"
    - "interpretability beyond classification output semantic bottleneck networks"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "methods for interpreting and understanding deep neural networks"
    - "semantic bottleneck for computer vision tasks"

Connected through hyperedge HE-6 (induced by source paper "functionality oriented convolutional filter pruning"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "revisiting the importance of individual units in cnns via ablation"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "counterfactual visual explanations"):
  with members:
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "embedding deep networks into visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "teaching categories to human learners with visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "visualizing deep neural network decisions prediction difference analysis"

Connected through hyperedge HE-8 (induced by source paper "predicting visual memory schemas with variational autoencoders"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "defining image memorability using the visual memory schema"
    - "amnet memorability estimation with attention"
    - "on the behavior of convolutional nets for feature extraction"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

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)