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: "super samples from kernel herding"
  Abstract: We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rate O(1/T) which is much faster than the usual O(1/pT) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.

Connected through hyperedge HE-1 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection"):
  with members:
    - "proving the lottery ticket hypothesis pruning is all you need"
    - "pruning from scratch"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "searching for mobilenetv3"
    - "data independent neural pruning via coresets"
    - "legr filter pruning via learned global ranking"
    - "what s hidden in a randomly weighted neural network"
    - "provable filter pruning for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "model reuse with reduced kernel mean embedding specification"):
  with members:
    - "federated machine learning concept and applications"
    - "algorithms and theory for multiple source adaptation"
    - "federated learning for mobile keyboard prediction"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "on the effectiveness of richardson extrapolation in machine learning"):
  with members:
    - "breaking the curse of dimensionality with convex neural networks"
    - "sharp analysis of low rank kernel matrix approximations"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-4 (induced by source paper "variational prototype replays for continual learning"):
  with members:
    - "few shot self reminder to overcome catastrophic forgetting"
    - "continuous learning in single incremental task scenarios"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "dynamic few shot visual learning without forgetting"
    - "memory aware synapses learning what not to forget"
    - "prototypical networks for few shot learning"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-5 (induced by source paper "splitting steepest descent for growing neural architectures"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "rethinking the value of network pruning"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"

Connected through hyperedge HE-6 (induced by source paper "gear geometry aware renyi information"):
  with members:
    - "stochastic wasserstein barycenters"
    - "improving gans using optimal transport"
    - "fixing a broken elbo"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improved techniques for training gans"
    - "maximizing diversity in biology and beyond"
    - "taming the noise in reinforcement learning via soft updates"

Connected through hyperedge HE-7 (induced by source paper "kernel conditional density operators"):
  with members:
    - "scaling gaussian process regression with derivatives"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "mmd gan towards deeper understanding of moment matching network"
    - "density estimation using real nvp"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "generative moment matching networks"
    - "adam a method for stochastic optimization"
    - "inverse density as an inverse problem the fredholm equation approach"

Connected through hyperedge HE-8 (induced by source paper "sub linear race sketches for approximate kernel density estimation on streaming data"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "hashing based estimators for kernel density in high dimensions"
    - "multi resolution hashing for fast pairwise summations"
    - "privacy via the johnson lindenstrauss transform"
    - "estimating continuous distributions in bayesian classifiers"

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)