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: "passcode parallel asynchronous stochastic dual co ordinate descent"
  Abstract: Stochastic Dual Coordinate Descent (SDCD) has become one of the most efficient ways to solve the family of $\ell_2$-regularized empirical risk minimization problems, including linear SVM, logistic regression, and many others. The vanilla implementation of DCD is quite slow; however, by maintaining primal variables while updating dual variables, the time complexity of SDCD can be significantly reduced. Such a strategy forms the core algorithm in the widely-used LIBLINEAR package. In this paper, we parallelize the SDCD algorithms in LIBLINEAR. In recent research, several synchronized parallel SDCD algorithms have been proposed, however, they fail to achieve good speedup in the shared memory multi-core setting. In this paper, we propose a family of asynchronous stochastic dual coordinate descent algorithms (ASDCD). Each thread repeatedly selects a random dual variable and conducts coordinate updates using the primal variables that are stored in the shared memory. We analyze the convergence properties when different locking/atomic mechanisms are applied. For implementation with atomic operations, we show linear convergence under mild conditions. For implementation without any atomic operations or locking, we present the first {\it backward error analysis} for ASDCD under the multi-core environment, showing that the converged solution is the exact solution for a primal problem with perturbed regularizer. Experimental results show that our methods are much faster than previous parallel coordinate descent solvers.

Connected through hyperedge HE-1 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "probabilistic synchronous parallel"
    - "revisiting distributed synchronous sgd"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "web scale topic models in spark an asynchronous parameter server"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-2 (induced by source paper "communication efficient asynchronous stochastic frank wolfe over nuclear norm balls"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "variance reduced and projection free stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-3 (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-4 (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"
    - "dimmwitted a study of main memory statistical analytics"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-5 (induced by source paper "minimizing latency for secure coded computing using secret sharing via staircase codes"):
  with members:
    - "analyzing the download time of availability codes"
    - "speeding up distributed machine learning using codes"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "private learning on networks part ii"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "revisiting distributed synchronous sgd"
    - "improving distributed gradient descent using reed solomon codes"

Connected through hyperedge HE-6 (induced by source paper "sparsified sgd with memory"):
  with members:
    - "communication compression for decentralized training"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "scaling sgd batch size to 32k for imagenet training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-7 (induced by source paper "fast variance reduction method with stochastic batch size"):
  with members:
    - "big batch sgd automated inference using adaptive batch sizes"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "variance reduced stochastic gradient descent with neighbors"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "large scale stochastic learning using gpus"):
  with members:
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "revisiting distributed synchronous sgd"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"

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)