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: "distributed k means and k median clustering on general topologies"
  Abstract: This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the problem of finding a clustering with low cost to the problem of finding a coreset of small size. We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies. Experimental results on large scale data sets show that this approach outperforms other coreset-based distributed clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing"):
  with members:
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "agnostic federated learning"
    - "clustering by orthogonal nmf model and non convex penalty optimization"
    - "secure federated matrix factorization"
    - "federated machine learning concept and applications"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"

Connected through hyperedge HE-3 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "making ai forget you data deletion in machine learning"):
  with members:
    - "quantized compressive k means"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed strongly convex optimization"
    - "scalable k means"
    - "fast distributed gradient methods"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "a quantum annealing based approach to extreme clustering"):
  with members:
    - "a hybrid quantum classical paradigm to mitigate embedding costs in quantum annealing"
    - "a novel graph based approach for determining molecular similarity"
    - "parallel streaming signature em tree a clustering algorithm for web scale applications"
    - "quantum annealing for clustering"

Connected through hyperedge HE-7 (induced by source paper "clustering by orthogonal nmf model and non convex penalty optimization"):
  with members:
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "greedy and local ratio algorithms in the mapreduce model"):
  with members:
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "space optimal semi streaming for 2 e approximate matching"
    - "streaming weighted matchings optimal meets greedy"
    - "randomized composable core sets for distributed submodular maximization"
    - "a new framework for distributed submodular maximization"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"

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)