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: "object recognition with and without objects"
  Abstract: While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural net- works on the foreground (object) and background (context) regions of images respectively. Consider- ing human recognition in the same situations, net- works trained on the pure background without ob- jects achieves highly reasonable recognition performance that beats humans by a large margin if only given context. However, humans still outperform networks with pure object available, which indicates networks and human beings have different mechanisms in understanding an image. Furthermore, we straightforwardly combine multiple trained networks to explore different visual cues learned by different networks. Experiments show that useful visual hints can be explicitly learned separately and then combined to achieve higher performance, which verifies the advantages of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-2 (induced by source paper "minimal images in deep neural networks fragile object recognition in natural images"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "exploring the landscape of spatial robustness"
    - "understanding deep learning requires rethinking generalization"
    - "yolo9000 better faster stronger"
    - "delving into transferable adversarial examples and black box attacks"
    - "on invariance and selectivity in representation learning"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "deep nets what have they ever done for vision"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the elephant in the room"
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "few shot image recognition by predicting parameters from activations"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "strike with a pose neural networks are easily fooled by strange poses of familiar objects"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "spatially transformed adversarial examples"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "geometric image synthesis"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"

Connected through hyperedge HE-5 (induced by source paper "unleashing the potential of cnns for interpretable few shot learning"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "few shot image recognition by predicting parameters from activations"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"

Connected through hyperedge HE-6 (induced by source paper "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "instance level salient object segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"

Connected through hyperedge HE-7 (induced by source paper "few shot learning by exploiting visual concepts within cnns"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "matching networks for one shot learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "object detection can be improved using human derived contextual expectations"):
  with members:
    - "seeing what is not there learning context to determine where objects are missing"
    - "semantic understanding of scenes through the ade20k dataset"
    - "a large scale car dataset for fine grained categorization and verification"
    - "contextual action recognition with r cnn"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "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)