human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "megatron lm training multi billion parameter language models using model parallelism"):
  Abstract: Recent work in unsupervised language modeling demonstrates that training large neural language models advances the state of the art in Natural Language Processing applications. However, for very large models, memory constraints limit the size of models that can be practically trained. Model parallelism allows us to train larger models, because the parameters can be split across multiple processors. In this work, we implement a simple, efficient intra-layer model parallel approach that enables training state of the art transformer language models with billions of parameters. Our approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We illustrate this approach by converging an 8.3 billion parameter transformer language model using 512 GPUs, making it the largest transformer model ever trained at 24x times the size of BERT and 5.6x times the size of GPT-2. We sustain up to 15.1 PetaFLOPs per second across the entire application with 76% scaling efficiency, compared to a strong single processor baseline that sustains 39 TeraFLOPs per second, which is 30% of peak FLOPs. The model is trained on 174GB of text, requiring 12 ZettaFLOPs over 9.2 days to converge. Transferring this language model achieves state of the art (SOTA) results on the WikiText103 (10.8 compared to SOTA perplexity of 16.4) and LAMBADA (66.5% compared to SOTA accuracy of 63.2%) datasets. We release training and evaluation code, as well as the weights of our smaller portable model, for reproducibility.

Includes members:
  Member "generalization through memorization nearest neighbor language models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realm retrieval augmented language model pre training")
  Member "exploring the limits of transfer learning with a unified text to text transformer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "dynamic evaluation of transformer language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sentencemim a latent variable language model")
  Member "spanbert improving pre training by representing and predicting spans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "multi task deep neural networks for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "albert a lite bert for self supervised learning of language representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "reducing bert pre training time from 3 days to 76 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling adaptive gradient methods from learning rates")

Question: Please tell me which class the source paper 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)