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: "cosine normalization using cosine similarity instead of dot product in neural networks"
  Abstract: Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change of input distribution, thus results in poor generalization, and aggravates the internal covariate shift which slows down the training. To bound dot product and decrease the variance, we propose to use cosine similarity or centered cosine similarity (Pearson Correlation Coefficient) instead of dot product in neural networks, which we call cosine normalization. We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN. Experiments show that cosine normalization achieves better performance than other normalization techniques.

Connected through hyperedge HE-1 (induced by source paper "extended batch normalization"):
  with members:
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "towards understanding regularization in batch normalization"
    - "generalized batch normalization towards accelerating deep neural networks"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (induced by source paper "an internal covariate shift bounding algorithm for deep neural networks by unitizing layers outputs"):
  with members:
    - "group normalization"
    - "decorrelated batch normalization"
    - "riemannian approach to batch normalization"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"
    - "identity mappings in deep residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "rethinking zero shot learning a conditional visual classification perspective"):
  with members:
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "residual non local attention networks for image restoration"
    - "visual semantic reasoning for image text matching"
    - "multimodal style transfer via graph cuts"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "selective zero shot classification with augmented attributes"
    - "discriminative learning of latent features for zero shot recognition"
    - "support neighbor loss for person re identification"

Connected through hyperedge HE-4 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-5 (induced by source paper "super identity convolutional neural network for face hallucination"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep hyperspherical learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep cascaded bi network for face hallucination"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "ring loss convex feature normalization for face recognition"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "a proximity aware hierarchical clustering of faces"
    - "range loss for deep face recognition with long tail"

Connected through hyperedge HE-7 (induced by source paper "active neural localization"):
  with members:
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural map structured memory for deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "qmdp net deep learning for planning under partial observability"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-8 (induced by source paper "normalization in training u net for 2d biomedical semantic segmentation"):
  with members:
    - "group normalization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "fully automatic detection and segmentation of abdominal aortic thrombus in post operative cta images using deep convolutional neural networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "towards automatic 3d shape instantiation for deployed stent grafts 2d multiple class and class imbalance marker segmentation with equally weighted focal u net"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "don t decay the learning rate increase the batch size"
    - "a survey on deep learning in medical image analysis"

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.LG(Machine Learning)