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: "how to scale up kernel methods to be as good as deep neural nets"
  Abstract: The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively overcome. In particular, we develop methods to scale up kernel models to successfully tackle large-scale learning problems that are so far only approachable by deep learning architectures. Based on the seminal work by Rahimi and Recht on approximating kernel functions with features derived from random projections, we advance the state-of-the-art by proposing methods that can efficiently train models with hundreds of millions of parameters, and learn optimal representations from multiple kernels. We conduct extensive empirical studies on problems from image recognition and automatic speech recognition, and show that the performance of our kernel models matches that of well-engineered deep neural nets (DNNs). To the best of our knowledge, this is the first time that a direct comparison between these two methods on large-scale problems is reported. Our kernel methods have several appealing properties: training with convex optimization, cost for training a single model comparable to DNNs, and significantly reduced total cost due to fewer hyperparameters to tune for model selection. Our contrastive study between these two very different but equally competitive models sheds light on fundamental questions such as how to learn good representations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "meta descent for online continual prediction"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding short horizon bias in stochastic meta optimization"
    - "tidbd adapting temporal difference step sizes through stochastic meta descent"
    - "online learning rate adaptation with hypergradient descent"
    - "effective sketching methods for value function approximation"
    - "learning to learn by gradient descent by gradient descent"
    - "accelerated gradient temporal difference learning"
    - "temporal difference networks"

Connected through hyperedge HE-3 (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"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "dirichlet based gaussian processes for large scale calibrated classification"):
  with members:
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "adversarial examples in the physical world"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-5 (induced by source paper "a continuation method for discrete optimization and its application to nearest neighbor classification"):
  with members:
    - "graph based time space trade offs for approximate near neighbors"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "breaking the curse of dimensionality with convex neural networks"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-6 (induced by source paper "revisiting random binning features fast convergence and strong parallelizability"):
  with members:
    - "a la carte learning fast kernels"
    - "scalable kernel methods via doubly stochastic gradients"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-7 (induced by source paper "an n log n parallel fast direct solver for kernel matrices"):
  with members:
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "scalable kernel methods via doubly stochastic gradients"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "gaussian quadrature for kernel features"):
  with members:
    - "convolutional sequence to sequence learning"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the error of random fourier features"
    - "convolutional neural networks for sentence classification"

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)