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: "giant globally improved approximate newton method for distributed optimization"
  Abstract: For distributed computing environments, we consider the canonical machine learning problem of empirical risk minimization (ERM) with quadratic regularization, and we propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, and then it sends this direction to the main driver. The driver, then, averages all the ANT directions received from workers to form a Globally Improved ANT (GIANT) direction. GIANT naturally exploits the trade-offs between local computations and global communications in that more local computations result in fewer overall rounds of communications. GIANT is highly communication efficient in that, for $d$-dimensional data uniformly distributed across $m$ workers, it has $4$ or $6$ rounds of communication and $O (d \log m)$ communication complexity per iteration. Theoretically, we show that GIANT's convergence rate is faster than first-order methods and existing distributed Newton-type methods. From a practical point-of-view, a highly beneficial feature of GIANT is that it has only one tuning parameter---the iterations of the local solver for computing an ANT direction. This is indeed in sharp contrast with many existing distributed Newton-type methods, as well as popular first-order methods, which have several tuning parameters, and whose performance can be greatly affected by the specific choices of such parameters. In this light, we empirically demonstrate the superior performance of GIANT compared with other competing methods.

Connected through hyperedge HE-1 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "convergence of distributed stochastic variance reduced methods without sampling extra data"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "federated optimization distributed optimization beyond the datacenter"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "distributed estimation of the inverse hessian by determinantal averaging"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "unbiased estimates for linear regression via volume sampling"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "federated optimization distributed machine learning for on device intelligence"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-4 (induced by source paper "federated learning via over the air computation"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "federated learning with non iid data"

Connected through hyperedge HE-5 (induced by source paper "newton admm a distributed gpu accelerated optimizer for multiclass classification problems"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "revisiting distributed synchronous sgd"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "adaptive consensus admm for distributed optimization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "dynanewton accelerating newton s method for machine learning"
    - "how to scale distributed deep learning"
    - "adaptive admm with spectral penalty parameter selection"

Connected through hyperedge HE-6 (induced by source paper "a distributed second order algorithm you can trust"):
  with members:
    - "cocoa a general framework for communication efficient distributed optimization"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "self concordant analysis for logistic regression"

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)