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: "low precision random fourier features for memory constrained kernel approximation"
  Abstract: We investigate how to train kernel approximation methods that generalize well under a memory budget. Building on recent theoretical work, we define a measure of kernel approximation error which we find to be more predictive of the empirical generalization performance of kernel approximation methods than conventional metrics. An important consequence of this definition is that a kernel approximation matrix must be high rank to attain close approximation. Because storing a high-rank approximation is memory intensive, we propose using a low-precision quantization of random Fourier features (LP-RFFs) to build a high-rank approximation under a memory budget. Theoretically, we show quantization has a negligible effect on generalization performance in important settings. Empirically, we demonstrate across four benchmark datasets that LP-RFFs can match the performance of full-precision RFFs and the Nystrom method, with 3x-10x and 50x-460x less memory, respectively.

Connected through hyperedge HE-1 (induced by source paper "on the downstream performance of compressed word embeddings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tensorized embedding layers for efficient model compression"
    - "low memory neural network training a technical report"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "high accuracy low precision training"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "word2ket space efficient word embeddings inspired by quantum entanglement"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "improving sequence to sequence learning via optimal transport"
    - "roberta a robustly optimized bert pretraining approach"
    - "the state of sparsity in deep neural networks"
    - "texar a modularized versatile and extensible toolkit for text generation"

Connected through hyperedge HE-3 (induced by source paper "making ai forget you data deletion in machine learning"):
  with members:
    - "quantized compressive k means"
    - "algorithms that remember model inversion attacks and data protection law"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "in datacenter performance analysis of a tensor processing unit"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "fixed point quantization of deep convolutional networks"
    - "training deep neural networks with low precision multiplications"
    - "distributed k means and k median clustering on general topologies"

Connected through hyperedge HE-4 (induced by source paper "random fourier features via fast surrogate leverage weighted sampling"):
  with members:
    - "nystrom landmark sampling and regularized christoffel functions"
    - "but how does it work in theory linear svm with random features"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "gaussian quadrature for kernel features"
    - "orthogonal random features"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

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)