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: "accelerating asynchronous stochastic gradient descent for neural machine translation"
  Abstract: In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size. Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor. We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.

Connected through hyperedge HE-1 (induced by source paper "making asynchronous stochastic gradient descent work for transformers"):
  with members:
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-2 (induced by source paper "a closer look at deep learning heuristics learning rate restarts warmup and distillation"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "three factors influencing minima in sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attention is all you need"
    - "snapshot ensembles train 1 get m for free"
    - "don t decay the learning rate increase the batch size"
    - "visualizing the loss landscape of neural nets"

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.CL(Computation and Language)