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: "hierarchical loss for classification"
  Abstract: Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called "loss" or "win") used in textual or visual classification/recognition via neural networks seldom view a sheepdog as more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier's classes.

Connected through hyperedge HE-1 (induced by source paper "simloss class similarities in cross entropy"):
  with members:
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "age progression regression by conditional adversarial autoencoder"
    - "adam a method for stochastic optimization"
    - "training convolutional networks with noisy labels"
    - "zero shot learning by convex combination of semantic embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "directional adversarial training for cost sensitive deep learning classification applications"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "adversarial vulnerability for any classifier"
    - "adversarially robust generalization requires more data"
    - "a mathematical theory of semantic development in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "label similarity curriculum learning"):
  with members:
    - "knowledge distillation for semi supervised domain adaptation"
    - "when does label smoothing help"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "automated curriculum learning for neural networks"
    - "deep mutual learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "hierarchical auxiliary learning"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "b cnn branch convolutional neural network for hierarchical classification"
    - "shake shake regularization"
    - "sgdr stochastic gradient descent with restarts"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "efficient path prediction for semi supervised and weakly supervised hierarchical text classification"):
  with members:
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (induced by source paper "snapshot distillation teacher student optimization in one generation"):
  with members:
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "label refinery improving imagenet classification through label progression"
    - "on calibration of modern neural networks"
    - "progressive neural architecture search"
    - "eraserelu a simple way to ease the training of deep convolution neural networks"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-7 (induced by source paper "knowledge distillation in generations more tolerant teachers educate better students"):
  with members:
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "sort second order response transform for visual recognition"
    - "mixup beyond empirical risk minimization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "shake shake regularization"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "label refinery improving imagenet classification through label progression"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "regularizing neural networks by penalizing confident output distributions"
    - "learning from noisy labels with distillation"
    - "disturblabel regularizing cnn on the loss layer"
    - "learning from simulated and unsupervised images through adversarial training"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "deep residual learning for image 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.LG(Machine Learning)