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: "error compensated quantized sgd and its applications to large scale distributed optimization"
  Abstract: Large-scale distributed optimization is of great importance in various applications. For data-parallel based distributed learning, the inter-node gradient communication often becomes the performance bottleneck. In this paper, we propose the error compensated quantized stochastic gradient descent algorithm to improve the training efficiency. Local gradients are quantized to reduce the communication overhead, and accumulated quantization error is utilized to speed up the convergence. Furthermore, we present theoretical analysis on the convergence behaviour, and demonstrate its advantage over competitors. Extensive experiments indicate that our algorithm can compress gradients by a factor of up to two magnitudes without performance degradation.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "optimal gradient quantization condition for communication efficient distributed training"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "federated learning strategies for improving communication efficiency"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "slsgd secure and efficient distributed on device machine learning"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"

Connected through hyperedge HE-4 (induced by source paper "on biased compression for distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "scaling distributed machine learning with in network aggregation"
    - "distributed learning with compressed gradient differences"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "natural compression for distributed deep learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"):
  with members:
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"
    - "sparcml high performance sparse communication for machine learning"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-7 (induced by source paper "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "on the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning"
    - "understanding top k sparsification in distributed deep learning"
    - "communication efficient distributed sgd with sketching"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"

Connected through hyperedge HE-8 (induced by source paper "election coding for distributed learning protecting signsgd against byzantine attacks"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "fast and secure distributed learning in high dimension"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"

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.CV(Computer Vision and Pattern Recognition)