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: "ensemble soft margin softmax loss for image classification"
  Abstract: Softmax loss is arguably one of the most popular losses to train CNN models for image classification. However, recent works have exposed its limitation on feature discriminability. This paper casts a new viewpoint on the weakness of softmax loss. On the one hand, the CNN features learned using the softmax loss are often inadequately discriminative. We hence introduce a soft-margin softmax function to explicitly encourage the discrimination between different classes. On the other hand, the learned classifier of softmax loss is weak. We propose to assemble multiple these weak classifiers to a strong one, inspired by the recognition that the diversity among weak classifiers is critical to a good ensemble. To achieve the diversity, we adopt the Hilbert-Schmidt Independence Criterion (HSIC). Considering these two aspects in one framework, we design a novel loss, named as Ensemble soft-Margin Softmax (EM-Softmax). Extensive experiments on benchmark datasets are conducted to show the superiority of our design over the baseline softmax loss and several state-of-the-art alternatives.

Connected through hyperedge HE-1 (induced by source paper "implicit semantic data augmentation for deep networks"):
  with members:
    - "convolutional networks with dense connectivity"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "autoaugment learning augmentation policies from data"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "attgan facial attribute editing by only changing what you want"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "mis classified vector guided softmax loss for face recognition"):
  with members:
    - "a high efficiency framework for constructing large scale face parsing benchmark"
    - "the devil of face recognition is in the noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "support vector guided softmax loss for face recognition"
    - "virtual class enhanced discriminative embedding learning"

Connected through hyperedge HE-3 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "class2str end to end latent hierarchy learning"
    - "do convolutional neural networks learn class hierarchy"
    - "deep forest towards an alternative to deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "casia surf a large scale multi modal benchmark for face anti spoofing"):
  with members:
    - "refineface refinement neural network for high performance face detection"
    - "mis classified vector guided softmax loss for face recognition"
    - "improved selective refinement network for face detection"
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-5 (induced by source paper "angular learning toward discriminative embedded features"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "one shot face recognition by promoting underrepresented classes"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "support vector guided softmax loss for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"

Connected through hyperedge HE-7 (induced by source paper "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "non local recurrent network for image restoration"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "a dataset and benchmark for large scale multi modal face anti spoofing"):
  with members:
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "support vector guided softmax loss for face recognition"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "sphereface deep hypersphere embedding for face recognition"

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)