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: "zero memory optimization towards training a trillion parameter models"
  Abstract: Training large DL models with billions and potentially trillions of parameters is challenging. Existing solutions exhibit fundamental limitations to obtain both memory and scaling (computation/communication) efficiency together. Data parallelism does not help reduce memory footprint per device: a model with 1.5 billion parameters or more runs out of memory. Model parallelism hardly scales efficiently beyond multiple devices of a single node due to fine-grained computation and expensive communication. We develop a novel solution, Zero Redundancy Optimizer (ZeRO), to optimize memory, achieving both memory efficiency and scaling efficiency. Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states instead, to scale the model size linearly with the number of devices. Furthermore, it retains scaling efficiency via computation and communication rescheduling and by reducing the model parallelism degree required to run large models. Our analysis on memory requirements and communication volume demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters using today's hardware (e.g., 1024 GPUs, 64 DGX-2 nodes). To meet near-term scaling goals and serve as a demonstration of ZeRO's capability, we implemented stage-1 optimizations of ZeRO (out of 3 stages in total described in the paper) and tested this ZeRO-OS version. ZeRO-OS reduces memory and boosts model size by 4x compared with the state-of-art, scaling up to 100B parameters. Moving forward, we will work on unlocking stage-2 optimizations, with up to 8x memory savings per device, and ultimately stage-3 optimizations, reducing memory linearly with respect to the number of devices and potentially scaling to models of arbitrary size. We are excited to transform very large models from impossible to train to feasible and efficient to train!

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"
    - "the hsic bottleneck deep learning without back propagation"

Connected through hyperedge HE-2 (induced by source paper "training large neural networks with constant memory using a new execution algorithm"):
  with members:
    - "reformer the efficient transformer"
    - "huggingface s transformers state of the art natural language processing"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "pipedream fast and efficient pipeline parallel dnn training"

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)