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: "snap ml a hierarchical framework for machine learning"
  Abstract: We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We prove theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. Additionally, we provide a review of the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and comparing with other widely-used machine learning software frameworks. Finally, we present a logistic regression benchmark on the Criteo Terabyte Click Logs dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results, including those obtained using TensorFlow and scikit-learn.

Connected through hyperedge HE-1 (induced by source paper "addressing algorithmic bottlenecks in elastic machine learning with chicle"):
  with members:
    - "elastic cocoa scaling in to improve convergence"
    - "don t use large mini batches use local sgd"
    - "slaq quality driven scheduling for distributed machine learning"
    - "measuring the effects of data parallelism on neural network training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "don t decay the learning rate increase the batch size"
    - "large batch training of convolutional networks"

Connected through hyperedge HE-2 (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"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "dimmwitted a study of main memory statistical analytics"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-3 (induced by source paper "breadth first depth next training of random forests"):
  with members:
    - "exact distributed training random forest with billions of examples"
    - "xgboost a scalable tree boosting system"

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)