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: "when does label smoothing help"
  Abstract: The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam-search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective. To explain these observations, we visualize how label smoothing changes the representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.

Connected through hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "towards federated learning at scale system design"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "federated adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"):
  with members:
    - "deep self learning from noisy labels"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "rethinking image mixture for unsupervised visual representation learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "improved baselines with momentum contrastive learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "bag of freebies for training object detection neural networks"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive study on the prediction reliability of graph neural networks for virtual screening"):
  with members:
    - "bayesian deep learning and a probabilistic perspective of generalization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "data efficient image recognition with contrastive predictive coding"
    - "self attention graph pooling"
    - "pre training graph neural networks"
    - "dropconnect is effective in modeling uncertainty of bayesian deep networks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "using pre training can improve model robustness and uncertainty"

Connected through hyperedge HE-5 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"
    - "heated up softmax embedding"

Connected through hyperedge HE-6 (induced by source paper "being bayesian about categorical probability"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "functional variational bayesian neural networks"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidential deep learning to quantify classification uncertainty"
    - "a variational dirichlet framework for out of distribution detection"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "enhancing the robustness of prior network in out of distribution detection"

Connected through hyperedge HE-7 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "measuring calibration in deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "non parametric calibration for classification"
    - "pytorch an imperative style high performance deep learning library"
    - "dirichlet based gaussian processes for large scale calibrated classification"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"

Connected through hyperedge HE-8 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "adversarial attacks on variational autoencoders"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"
    - "certified defenses against adversarial examples"

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)