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: "simple and deterministic matrix sketching"
  Abstract: We adapt a well known streaming algorithm for approximating item frequencies to the matrix sketching setting. The algorithm receives the rows of a large matrix $A \in \R^{n \times m}$ one after the other in a streaming fashion. It maintains a sketch matrix $B \in \R^ {1/\eps \times m}$ such that for any unit vector $x$ [\|Ax\|^2 \ge \|Bx\|^2 \ge \|Ax\|^2 - \eps \|A\|_{f}^2 \.] Sketch updates per row in $A$ require $O(m/\eps^2)$ operations in the worst case. A slight modification of the algorithm allows for an amortized update time of $O(m/\eps)$ operations per row. The presented algorithm stands out in that it is: deterministic, simple to implement, and elementary to prove. It also experimentally produces more accurate sketches than widely used approaches while still being computationally competitive.

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-2 (induced by source paper "filter sketch for network pruning"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "play and prune adaptive filter pruning for deep model compression"
    - "bilinear attention networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "data augmentation for personal knowledge graph population"):
  with members:
    - "a neural architecture for person ontology population"
    - "position aware graph neural networks"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fine grained classification of personal data entities"
    - "counterfactual fairness in text classification through robustness"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "context dependent fine grained entity type tagging"

Connected through hyperedge HE-4 (induced by source paper "a deterministic streaming sketch for ridge regression"):
  with members:
    - "robust frequent directions with application in online learning"
    - "efficient frequent directions algorithm for sparse matrices"
    - "online row sampling"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-5 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-6 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-7 (induced by source paper "continual learning via online leverage score sampling"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "neural processes"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-8 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

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.DS(Data Structures and Algorithms)