human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "second order democratic aggregation"):
  Abstract: Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets.

Includes members:
  Member "a deeper look at power normalizations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "statistically motivated second order pooling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "is second order information helpful for large scale visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "improved bilinear pooling with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "second order convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications")
  Member "tensor representations via kernel linearization for action recognition from 3d skeletons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "interferences in match kernels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep generalized max pooling")
  Member "compact bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "component analysis for visual question answering architectures")

Question: Please tell me which class the source paper 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)