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: "monet moments embedding network"
  Abstract: Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks. Different from conventional global average pooling or fully connected layer, bilinear pooling gathers 2nd order information in a translation invariant fashion. However, a serious drawback of this family of pooling layers is their dimensionality explosion. Approximate pooling methods with compact properties have been explored towards resolving this weakness. Additionally, recent results have shown that significant performance gains can be achieved by adding 1st order information and applying matrix normalization to regularize unstable higher order information. However, combining compact pooling with matrix normalization and other order information has not been explored until now. In this paper, we unify bilinear pooling and the global Gaussian embedding layers through the empirical moment matrix. In addition, we propose a novel sub-matrix square-root layer, which can be used to normalize the output of the convolution layer directly and mitigate the dimensionality problem with off-the-shelf compact pooling methods. Our experiments on three widely used fine-grained classification datasets illustrate that our proposed architecture, MoNet, can achieve similar or better performance than with the state-of-art G2DeNet. Furthermore, when combined with compact pooling technique, MoNet obtains comparable performance with encoded features with 96% less dimensions.

Connected through hyperedge HE-1 (induced by source paper "invariant tensor feature coding"):
  with members:
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "group invariant deep representations for image instance retrieval"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "semantic bilinear pooling for fine grained recognition"):
  with members:
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "low rank bilinear pooling for fine grained classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "fine grained image classification by exploring bipartite graph labels"
    - "compact bilinear pooling"

Connected through hyperedge HE-3 (induced by source paper "compact approximation for polynomial of covariance feature"):
  with members:
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "fine grained visual classification of aircraft"

Connected through hyperedge HE-4 (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"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "statistically motivated second order pooling"
    - "part aligned bilinear representations for person re identification"

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)