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: "statistically motivated second order pooling"
  Abstract: Second-order pooling, a.k.a. bilinear pooling, has proven effective for visual recognition. The recent progress in this area has focused on either designing normalization techniques for second-order models, or compressing the second-order representations. However, these two directions have typically been followed separately, and without any clear statistical motivation. Here, by contrast, we introduce a statistically-motivated framework that jointly tackles normalization and compression of second-order representations. To this end, we design a parametric vectorization layer, which maps a covariance matrix, known to follow a Wishart distribution, to a vector whose elements can be shown to follow a Chi-square distribution. We then propose to make use of a square-root normalization, which makes the distribution of the resulting representation converge to a Gaussian, thus complying with the standard machine learning assumption. As evidenced by our experiments, this lets us outperform the state-of-the-art second-order models on several benchmark recognition datasets.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (induced by source paper "learning to synthesize fashion textures"):
  with members:
    - "diagnosing and enhancing vae models"
    - "spectral normalization for generative adversarial networks"
    - "dist gan an improved gan using distance constraints"
    - "viton an image based virtual try on network"
    - "progressive growing of gans for improved quality stability and variation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-3 (induced by source paper "local temporal bilinear pooling for fine grained action parsing"):
  with members:
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "compact generalized non local network"
    - "temporal human action segmentation via dynamic clustering"
    - "monet moments embedding network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-4 (induced by source paper "second order democratic aggregation"):
  with members:
    - "a deeper look at power normalizations"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "second order convolutional neural networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "interferences in match kernels"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-5 (induced by source paper "deep attentional structured representation learning for visual recognition"):
  with members:
    - "improved bilinear pooling with cnns"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "deep fishernet for object classification"
    - "compact bilinear pooling"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

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)