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: "decoupled networks"
  Abstract: Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation that CNN-learned features are naturally decoupled with the norm of features corresponding to the intra-class variation and the angle corresponding to the semantic difference, we propose a generic decoupled learning framework which models the intra-class variation and semantic difference independently. Specifically, we first reparametrize the inner product to a decoupled form and then generalize it to the decoupled convolution operator which serves as the building block of our decoupled networks. We present several effective instances of the decoupled convolution operator. Each decoupled operator is well motivated and has an intuitive geometric interpretation. Based on these decoupled operators, we further propose to directly learn the operator from data. Extensive experiments show that such decoupled reparameterization renders significant performance gain with easier convergence and stronger robustness.

Connected through hyperedge HE-1 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "meta architecture search"

Connected through hyperedge HE-2 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "deep randomized ensembles for metric learning"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-3 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-4 (induced by source paper "kervolutional neural networks"):
  with members:
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "non linear convolution filters for cnn based learning"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-6 (induced by source paper "compressive hyperspherical energy minimization"):
  with members:
    - "group normalization"
    - "learning towards minimum hyperspherical energy"
    - "darts differentiable architecture search"
    - "deep semi random features for nonlinear function approximation"
    - "diversity promoting bayesian learning of latent variable models"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"

Connected through hyperedge HE-7 (induced by source paper "fix your features stationary and maximally discriminative embeddings using regular polytope fixed classifier networks"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning towards minimum hyperspherical energy"
    - "additive margin softmax for face verification"
    - "improving generalization via scalable neighborhood component analysis"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning towards minimum hyperspherical energy"):
  with members:
    - "orthogonality promoting distance metric learning convex relaxation and theoretical analysis"
    - "the devil of face recognition is in the noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "spectral normalization for generative adversarial networks"
    - "additive margin softmax for face verification"
    - "diversity promoting bayesian learning of latent variable models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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)