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: "scaling back propagation by parallel scan algorithm"
  Abstract: In an era when the performance of a single compute device plateaus, software must be designed to scale on a massively parallel system for better runtime performance. However, the commonly used back-propagation (BP) algorithm imposes a strong sequential dependency in the process of gradient computation. Under model parallelism, BP has a theoretical step complexity of $\Theta (n)$ which hinders its scalability in a parallel computing environment, where $n$ represents the number of compute devices into which a model is partitioned. In this work, we restructure such dependency and reformulate BP into a scan operation which is scaled by our modified version of the Blelloch scan algorithm. Our algorithm is able to achieve a theoretical step complexity of $\Theta (\log n)$. We perform an in-depth performance analysis and identify the challenges of deploying our algorithm in a practical setting, along with a variety of approaches to tackle such challenges. We demonstrate the scalability benefits of our algorithm in the use case of retraining pruned networks.

Connected through hyperedge HE-1 (induced by source paper "an optimized and energy efficient parallel implementation of non iteratively trained recurrent neural networks"):
  with members:
    - "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"
    - "memory visualization for gated recurrent neural networks in speech recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep learning in neural networks"
    - "attention based models for speech recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)