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: "variance based gradient compression for efficient distributed deep learning"
  Abstract: Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.

Connected through hyperedge HE-1 (induced by source paper "think locally act globally federated learning with local and global representations"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "improved adversarial learning for fair classification"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "rpn a residual pooling network for efficient federated learning"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "signsgd compressed optimisation for non convex problems"
    - "expanding the reach of federated learning by reducing client resource requirements"
    - "sparse communication for distributed gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "very deep convolutional networks for end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"

Connected through hyperedge HE-4 (induced by source paper "robust and communication efficient federated learning from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "compact and computationally efficient representation of deep neural networks"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "how to backdoor federated learning"

Connected through hyperedge HE-5 (induced by source paper "federated learning over wireless fading channels"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "don t use large mini batches use local sgd"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-6 (induced by source paper "deep leakage from gradients"):
  with members:
    - "towards federated learning at scale system design"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-7 (induced by source paper "sparse binary compression towards distributed deep learning with minimal communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "compact and computationally efficient representation of deep neural networks"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed 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)