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: "a reliable effective terascale linear learning system"
  Abstract: We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers to the number of non-zero entries in the data matrix.} billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques are new, but the careful synthesis required to obtain an efficient implementation is. The result is, up to our knowledge, the most scalable and efficient linear learning system reported in the literature (as of 2011 when our experiments were conducted). We describe and thoroughly evaluate the components of the system, showing the importance of the various design choices.

Connected through hyperedge HE-1 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage"):
  with members:
    - "age of information in multiple sensing"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"

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 "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-5 (induced by source paper "analysis of large scale multi tenant gpu clusters for dnn training workloads"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "slaq quality driven scheduling for distributed machine learning"
    - "attention is all you need"
    - "deepprof performance analysis for deep learning applications via mining gpu execution patterns"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "distributed word2vec using graph analytics frameworks"):
  with members:
    - "code2vec learning distributed representations of code"
    - "code2seq generating sequences from structured representations of code"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "distributed representations for biological sequence analysis"
    - "parallelizing word2vec in shared and distributed memory"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "decentralized federated learning a segmented gossip approach"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning strategies for improving communication efficiency"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gossip training for deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-8 (induced by source paper "distributed function minimization in apache spark"):
  with members:
    - "parallel streaming wasserstein barycenters"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "graphlab a new framework for parallel machine learning"

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)