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: "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
  Abstract: With the development of convolutional neural networks (CNNs) in recent years, the network structure has become more and more complex and varied, and has achieved very good results in pattern recognition, image classification, object detection and tracking. For CNNs used for image classification, in addition to the network structure, more and more research is now focusing on the improvement of the loss function, so as to enlarge the inter-class feature differences, and reduce the intra-class feature variations as soon as possible. Besides the traditional Softmax, typical loss functions include L-Softmax, AM-Softmax, ArcFace, and Center loss, etc. Based on the concept of predefined evenly-distributed class centroids (PEDCC) in CSAE network, this paper proposes a PEDCC-based loss function called PEDCC-Loss, which can make the inter-class distance maximal and intra-class distance small enough in hidden feature space. Multiple experiments on image classification and face recognition have proved that our method achieve the best recognition accuracy, and network training is stable and easy to converge. Code is available in this https URL

Connected through hyperedge HE-1 (induced by source paper "semi supervised learning method based on predefined evenly distributed class centroids"):
  with members:
    - "a classification supervised auto encoder based on predefined evenly distributed class centroids"
    - "mixmatch a holistic approach to semi supervised learning"
    - "additive margin softmax for face verification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a comparison of super resolution and nearest neighbors interpolation applied to object detection on satellite data"):
  with members:
    - "xview objects in context in overhead imagery"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "cofga classification of fine grained features in aerial images"
    - "deep back projection networks for super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-3 (induced by source paper "incremental classifier learning based on pedcc loss and cosine distance"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "a strategy for an uncompromising incremental learner"
    - "progressive neural networks"
    - "wide residual networks"
    - "conditional generative adversarial nets"

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)