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: "what do deep networks like to see"
  Abstract: We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters. We compared the reconstructed samples from AEs that were fine-tuned on a set of image classifiers (AlexNet, VGG16, ResNet-50, and Inception~v3) and found substantial differences. The AE learns which aspects of the input space to preserve and which ones to ignore, based on the information encoded in the backpropagated gradients. Measuring the changes in accuracy when the signal of one classifier is used by a second one, a relation of total order emerges. This order depends directly on each classifier's input signal but it does not correlate with classification accuracy or network size. Further evidence of this phenomenon is provided by measuring the normalized mutual information between original images and auto-encoded reconstructions from different fine-tuned AEs. These findings break new ground in the area of neural network understanding, opening a new way to reason, debug, and interpret their results. We present four concrete examples in the literature where observations can now be explained in terms of the input signal that a model uses.

Connected through hyperedge HE-1 (induced by source paper "lip local importance based pooling"):
  with members:
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "scale aware trident networks for object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "detnet a backbone network for object detection"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "detail preserving pooling in deep networks"

Connected through hyperedge HE-2 (induced by source paper "what do deep networks like to read"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "interpretable neural predictions with differentiable binary variables"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "analysis methods in neural language processing a survey"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-3 (induced by source paper "refining 6d object pose predictions using abstract render and compare"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "light weight refinenet for real time semantic segmentation"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "neural 3d mesh renderer"

Connected through hyperedge HE-4 (induced by source paper "similarity preserving image image domain adaptation for person re identification"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "low shot learning from imaginary data"
    - "cycada cycle consistent adversarial domain adaptation"
    - "demystifying neural style transfer"
    - "good semi supervised learning that requires a bad gan"
    - "camera style adaptation for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-5 (induced by source paper "an overview of computational approaches for analyzing interpretation"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "multimodal social media analysis for gang violence prevention"
    - "adversarial defense based on structure to signal autoencoders"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-6 (induced by source paper "an overview of computational approaches for interpretation analysis"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "adversarial defense based on structure to signal autoencoders"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"
    - "interpreting deep visual representations via network dissection"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-7 (induced by source paper "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"):
  with members:
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "predictive inequity in object detection"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "exploring disentangled feature representation beyond face identification"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "a reductions approach to fair classification"
    - "learning gender neutral word embeddings"

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)