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: "mini batch primal and dual methods for svms"
  Abstract: We address the issue of using mini-batches in stochastic optimization of SVMs. We show that the same quantity, the spectral norm of the data, controls the parallelization speedup obtained for both primal stochastic subgradient descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it to derive novel variants of mini-batched SDCA. Our guarantees for both methods are expressed in terms of the original nonsmooth primal problem based on the hinge-loss.

Connected through hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient federated learning over wireless communication networks"
    - "supervised learning under distributed features"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "revisiting distributed synchronous sgd"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "adding vs averaging in distributed primal dual optimization"
    - "fast distributed asynchronous sgd with variance reduction"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-4 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication lower bounds for distributed convex optimization partition data on features"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-5 (induced by source paper "the non iid data quagmire of decentralized machine learning"):
  with members:
    - "group normalization"
    - "understanding batch normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "sdca without duality regularization and individual convexity"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "variance reduced stochastic gradient descent with neighbors"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-7 (induced by source paper "don t use large mini batches use local sgd"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "an empirical model of large batch training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

Connected through hyperedge HE-8 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "confidence prediction for lexicon free ocr"
    - "vital visual tracking via adversarial learning"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "synthetic data generation for indic handwritten text recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "adversarial discriminative domain adaptation"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"

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)