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: "an empirical evaluation of current convolutional architectures ability to manage nuisance location and scale variability"
  Abstract: We conduct an empirical study to test the ability of Convolutional Neural Networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.

Connected through hyperedge HE-1 (induced by source paper "assessing capsule networks with biased data"):
  with members:
    - "capsule networks for low data transfer learning"
    - "impact of biases in big data"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "dynamic routing between capsules"
    - "emnist an extension of mnist to handwritten letters"
    - "evaluating deep convolutional neural networks for material classification"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-2 (induced by source paper "with friends like these who needs adversaries"):
  with members:
    - "classification regions of deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "mixup beyond empirical risk minimization"
    - "analysis of universal adversarial perturbations"
    - "opening the black box of deep neural networks via information"
    - "measuring the tendency of cnns to learn surface statistical regularities"

Connected through hyperedge HE-3 (induced by source paper "empirically analyzing the effect of dataset biases on deep face recognition systems"):
  with members:
    - "dynamic routing between capsules"
    - "opening the black box of deep neural networks via information"
    - "morphable face models an open framework"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "group equivariant convolutional networks"
    - "a lightened cnn for deep face representation"
    - "facenet a unified embedding for face recognition and clustering"

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)