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: "destnet densely fused spatial transformer networks"
  Abstract: Modern Convolutional Neural Networks (CNN) are extremely powerful on a range of computer vision tasks. However, their performance may degrade when the data is characterised by large intra-class variability caused by spatial transformations. The Spatial Transformer Network (STN) is currently the method of choice for providing CNNs the ability to remove those transformations and improve performance in an end-to-end learning framework. In this paper, we propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best knowledge, is the first dense fusion pattern for combining multiple STNs. Specifically, we show how changing the connectivity pattern of multiple STNs from sequential to dense leads to more powerful alignment modules. Extensive experiments on three benchmarks namely, MNIST, GTSRB, and IDocDB show that the proposed technique outperforms related state-of-the-art methods (i.e., STNs and CSTNs) both in terms of accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "explicit disentanglement of appearance and perspective in generative models"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "isolating sources of disentanglement in variational autoencoders"
    - "towards a definition of disentangled representations"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "improving variational inference with inverse autoregressive flow"

Connected through hyperedge HE-2 (induced by source paper "jointly aligning millions of images with deep penalised reconstruction congealing"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "face detection using deep learning an improved faster rcnn approach"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "large scale image retrieval with attentive deep local features"
    - "inverse compositional spatial transformer networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "supervised transformer network for efficient face detection"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

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)