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: "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
  Abstract: We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralized algorithm with non-blocking communication between processors. One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion. As a consequence it is a lock-free parallel algorithm. In spite of being an asynchronous algorithm, the variable updates of NOMAD are serializable, that is, there is an equivalent update ordering in a serial implementation. NOMAD outperforms synchronous algorithms which require explicit bulk synchronization after every iteration: our extensive empirical evaluation shows that not only does our algorithm perform well in distributed setting on commodity hardware, but also outperforms state-of-the-art algorithms on a HPC cluster both in multi-core and distributed memory settings.

Connected through hyperedge HE-1 (induced by source paper "machine learning on volatile instances"):
  with members:
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "revisiting distributed synchronous sgd"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "efficient straggler replication in large scale parallel computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "dynamic parameter allocation in parameter servers"):
  with members:
    - "priority based parameter propagation for distributed dnn training"
    - "deep contextualized word representations"
    - "open world knowledge graph completion"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "analogical inference for multi relational embeddings"
    - "holographic embeddings of knowledge graphs"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving point of interest recommendation using decentralized matrix factorization"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-4 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-5 (induced by source paper "graphvite a high performance cpu gpu hybrid system for node embedding"):
  with members:
    - "bmf block matrix approach to factorization of large scale data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "mixed precision training"
    - "parallelizing word2vec in multi core and many core architectures"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-6 (induced by source paper "on the convergence of perturbed distributed asynchronous stochastic gradient descent to second order stationary points in non convex optimization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "global optimality in low rank matrix optimization"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "deep learning with elastic averaging sgd"

Connected through hyperedge HE-7 (induced by source paper "understanding and improving proximity graph based maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "to index or not to index optimizing exact maximum inner product search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "a greedy approach for budgeted maximum inner product search"
    - "quantization based fast inner product search"
    - "fast neighborhood graph search using cartesian concatenation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "norm explicit quantization improving vector quantization for maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "billion scale similarity search with gpus"
    - "clustering is efficient for approximate maximum inner product search"
    - "quantization based fast inner product search"

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.DC(Distributed, Parallel, and Cluster Computing)