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: "how much position information do convolutional neural networks encode"
  Abstract: In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so. In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks. A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs.

Connected through hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "disentangling monocular 3d object detection"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"
    - "canonical surface mapping via geometric cycle consistency"

Connected through hyperedge HE-2 (induced by source paper "do cnns encode data augmentations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial examples improve image recognition"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "regularized evolution for image classifier architecture search"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-3 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild"):
  with members:
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "high resolution representations for labeling pixels and regions"
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "style aggregated network for facial landmark detection"

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)