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: "feature incay for representation regularization"
  Abstract: Softmax loss is widely used in deep neural networks for multi-class classification, where each class is represented by a weight vector, a sample is represented as a feature vector, and the feature vector has the largest projection on the weight vector of the correct category when the model correctly classifies a sample. To ensure generalization, weight decay that shrinks the weight norm is often used as regularizer. Different from traditional learning algorithms where features are fixed and only weights are tunable, features are also tunable as representation learning in deep learning. Thus, we propose feature incay to also regularize representation learning, which favors feature vectors with large norm when the samples can be correctly classified. With the feature incay, feature vectors are further pushed away from the origin along the direction of their corresponding weight vectors, which achieves better inter-class separability. In addition, the proposed feature incay encourages intra-class compactness along the directions of weight vectors by increasing the small feature norm faster than the large ones. Empirical results on MNIST, CIFAR10 and CIFAR100 demonstrate feature incay can improve the generalization ability.

Connected through hyperedge HE-1 (induced by source paper "fix your features stationary and maximally discriminative embeddings using regular polytope fixed classifier networks"):
  with members:
    - "decoupled networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "decoupled networks"):
  with members:
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "additive margin softmax for face verification"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "deep hyperspherical learning"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)