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: "parallel coordinate descent for l1 regularized loss minimization"
  Abstract: We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict linear speedups, up to a problem-dependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L1.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "on linear learning with manycore processors"):
  with members:
    - "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"
    - "partitioning compute units in cnn acceleration for statistical memory traffic shaping"
    - "faster coordinate descent via adaptive importance sampling"
    - "scaling deep learning on gpu and knights landing clusters"
    - "efficient use of limited memory accelerators for linear learning on heterogeneous systems"
    - "safe adaptive importance sampling"
    - "primal dual rates and certificates"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "syscd a system aware parallel coordinate descent algorithm"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "elastic cocoa scaling in to improve convergence"
    - "snap ml a hierarchical framework for machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "dimmwitted a study of main memory statistical analytics"

Connected through hyperedge HE-4 (induced by source paper "stochastic distributed optimization for machine learning from decentralized features"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-5 (induced by source paper "revisiting random binning features fast convergence and strong parallelizability"):
  with members:
    - "a la carte learning fast kernels"
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-7 (induced by source paper "terngrad ternary gradients to reduce communication in distributed deep learning"):
  with members:
    - "learning intrinsic sparse structures within long short term memory"
    - "revisiting distributed synchronous sgd"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "recurrent neural networks with limited numerical precision"

Connected through hyperedge HE-8 (induced by source paper "parmac distributed optimisation of nested functions with application to binary autoencoders"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "learning hashing with affinity based loss functions using auxiliary coordinates"
    - "hashing with binary autoencoders"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "deep learning with limited numerical precision"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "distributed optimization of deeply nested systems"

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)