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: "sparsifying neural network connections for face recognition"
  Abstract: This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-the-art DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.

Connected through hyperedge HE-1 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-2 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-4 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-5 (induced by source paper "smaller models better generalization"):
  with members:
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "noiseout a simple way to prune neural networks"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "nodedrop a condition for reducing network size without effect on output"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning the number of neurons in deep networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-7 (induced by source paper "softmax dissection towards understanding intra and inter class objective for embedding learning"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "homocentric hypersphere feature embedding for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep metric learning with angular loss"
    - "l2 constrained softmax loss for discriminative face verification"
    - "level playing field for million scale face recognition"

Connected through hyperedge HE-8 (induced by source paper "continual learning via neural pruning"):
  with members:
    - "superposition of many models into one"
    - "the state of sparsity in deep neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "power law in sparsified deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "thinet a filter level pruning method for deep neural network compression"

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)