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 "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  Abstract: Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, Megatron Language Model with 8.3B parameters was trained on a GPU cluster worth \$25 million. As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of ten thousand \$2500 desktops dwarfs that of a \$25M server pod, but one cannot utilize that power efficiently with conventional distributed training methods. In this work, we propose Learning@home: a neural network training paradigm designed to handle millions of poorly connected participants. We analyze the performance, reliability, and architectural constraints of this paradigm and compare it against existing distributed training techniques.

Includes members:
  Member "scaling laws for neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "zero memory optimization towards training a trillion parameter models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training large neural networks with constant memory using a new execution algorithm")
  Member "megatron lm training multi billion parameter language models using model parallelism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "natural compression for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor")
  Member "defending against neural fake news":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "large scale learning of general visual representations for transfer":
    also connected through other hyperedges:
      (none)
  Member "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mlperf training benchmark")
  Member "pipemare asynchronous pipeline parallel dnn training":
    also connected through other hyperedges:
      (none)

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.DC(Distributed, Parallel, and Cluster Computing)