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: "on loss functions for deep neural networks in classification"
  Abstract: Deep neural networks are currently among the most commonly used classifiers. Despite easily achieving very good performance, one of the best selling points of these models is their modular design - one can conveniently adapt their architecture to specific needs, change connectivity patterns, attach specialised layers, experiment with a large amount of activation functions, normalisation schemes and many others. While one can find impressively wide spread of various configurations of almost every aspect of the deep nets, one element is, in authors' opinion, underrepresented - while solving classification problems, vast majority of papers and applications simply use log loss. In this paper we try to investigate how particular choices of loss functions affect deep models and their learning dynamics, as well as resulting classifiers robustness to various effects. We perform experiments on classical datasets, as well as provide some additional, theoretical insights into the problem. In particular we show that L1 and L2 losses are, quite surprisingly, justified classification objectives for deep nets, by providing probabilistic interpretation in terms of expected misclassification. We also introduce two losses which are not typically used as deep nets objectives and show that they are viable alternatives to the existing ones.

Connected through hyperedge HE-1 (induced by source paper "population based training for loss function optimization"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "nsga net neural architecture search using multi objective genetic algorithm"

Connected through hyperedge HE-2 (induced by source paper "gradient based adversarial training on transformer networks for detecting check worthy factual claims"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "how to fine tune bert for text classification"
    - "claimrank detecting check worthy claims in arabic and english"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"

Connected through hyperedge HE-3 (induced by source paper "designing gans a likelihood ratio approach"):
  with members:
    - "lipschitz generative adversarial nets"
    - "generative adversarial networks an overview"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "energy based generative adversarial network"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-4 (induced by source paper "deep inverse feature learning a representation learning of error"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "regularization for deep learning a taxonomy"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "xgboost a scalable tree boosting system"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-5 (induced by source paper "optimal transfer learning model for binary classification of funduscopic images through simple heuristics"):
  with members:
    - "an introduction to domain adaptation and transfer learning"
    - "dynamic routing between capsules"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (induced by source paper "correlation maximized structural similarity loss for semantic segmentation"):
  with members:
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "focal loss for dense object detection"
    - "learning affinity via spatial propagation networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stacked deconvolutional network for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "feature intertwiner for object detection"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "neural network encapsulation"
    - "gradient harmonized single stage detector"
    - "grid r cnn"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "improving gans using optimal transport"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "brain tumor segmentation with deep neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual path networks"

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)