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: "scalable kernel methods via doubly stochastic gradients"
  Abstract: The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional gradients". Our approach relies on the fact that many kernel methods can be expressed as convex optimization problems, and we solve the problems by making two unbiased stochastic approximations to the functional gradient, one using random training points and another using random functions associated with the kernel, and then descending using this noisy functional gradient. We show that a function produced by this procedure after $t$ iterations converges to the optimal function in the reproducing kernel Hilbert space in rate $O(1/t)$, and achieves a generalization performance of $O(1/\sqrt{t})$. This doubly stochasticity also allows us to avoid keeping the support vectors and to implement the algorithm in a small memory footprint, which is linear in number of iterations and independent of data dimension. Our approach can readily scale kernel methods up to the regimes which are dominated by neural nets. We show that our method can achieve competitive performance to neural nets in datasets such as 8 million handwritten digits from MNIST, 2.3 million energy materials from MolecularSpace, and 1 million photos from ImageNet.

Connected through hyperedge HE-1 (induced by source paper "neural kernels without tangents"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "do imagenet classifiers generalize to imagenet"
    - "on exact computation with an infinitely wide neural net"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a kernel theory of modern data augmentation"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "sparsified sgd with memory"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "orthogonal random features"
    - "communication efficient learning of deep networks from decentralized data"
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-3 (induced by source paper "no training required exploring random encoders for sentence classification"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"

Connected through hyperedge HE-4 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"

Connected through hyperedge HE-5 (induced by source paper "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  with members:
    - "policy optimization as wasserstein gradient flows"
    - "a convex duality framework for gans"
    - "adversarial distillation of bayesian neural network posteriors"
    - "on wasserstein reinforcement learning and the fokker planck equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "boosting the actor with dual critic"
    - "learning from conditional distributions via dual embeddings"

Connected through hyperedge HE-6 (induced by source paper "scalable kernel learning via the discriminant information"):
  with members:
    - "mmd gan towards deeper understanding of moment matching network"
    - "adam a method for stochastic optimization"
    - "a la carte learning fast kernels"
    - "convolutional kernel networks"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "quadruply stochastic gradient method for large scale nonlinear semi supervised ordinal regression auc optimization"):
  with members:
    - "quadruply stochastic gradients for large scale nonlinear semi supervised auc optimization"
    - "semi supervised ordinal regression based on empirical risk minimization"
    - "scalable semi supervised svm via triply stochastic gradients"
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-8 (induced by source paper "choicenet robust learning by revealing output correlations"):
  with members:
    - "deep learning is robust to massive label noise"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "building robust deep neural networks for road sign detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "mixup beyond empirical risk minimization"
    - "decoupling when to update from how to update"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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)