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: "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
  Abstract: Many of our core assumptions about how neural networks operate remain empirically untested. One common assumption is that convolutional neural networks need to be stable to small translations and deformations to solve image recognition tasks. For many years, this stability was baked into CNN architectures by incorporating interleaved pooling layers. Recently, however, interleaved pooling has largely been abandoned. This raises a number of questions: Are our intuitions about deformation stability right at all? Is it important? Is pooling necessary for deformation invariance? If not, how is deformation invariance achieved in its absence? In this work, we rigorously test these questions, and find that deformation stability in convolutional networks is more nuanced than it first appears: (1) Deformation invariance is not a binary property, but rather that different tasks require different degrees of deformation stability at different layers. (2) Deformation stability is not a fixed property of a network and is heavily adjusted over the course of training, largely through the smoothness of the convolutional filters. (3) Interleaved pooling layers are neither necessary nor sufficient for achieving the optimal form of deformation stability for natural image classification. (4) Pooling confers too much deformation stability for image classification at initialization, and during training, networks have to learn to counteract this inductive bias. Together, these findings provide new insights into the role of interleaved pooling and deformation invariance in CNNs, and demonstrate the importance of rigorous empirical testing of even our most basic assumptions about the working of neural networks.

Connected through hyperedge HE-1 (induced by source paper "making convolutional networks shift invariant again"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "spatially transformed adversarial examples"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "polar transformer networks"

Connected through hyperedge HE-2 (induced by source paper "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "pairwise confusion for fine grained visual classification"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "opening the black box of deep learning"):
  with members:
    - "decoupled networks"
    - "a mathematical framework for superintelligent machines"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "what do we understand about convolutional networks"
    - "large scale distributed neural network training through online distillation"
    - "deep learning a critical appraisal"
    - "mathematics of deep learning"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial network architectures for image synthesis using capsule networks"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "dynamic routing between capsules"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "from facial parts responses to face detection a deep learning approach"

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)