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: "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression"
  Abstract: A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nodes on a network. Communication is a major bottleneck in such applications, and in recent years, compressed stochastic gradient methods such as QSGD (quantized SGD) and sparse SGD have been proposed to reduce communication. It was also shown that error compensation can be combined with compression to achieve better convergence in a scheme that each node compresses its local stochastic gradient and broadcast the result to all other nodes over the network in a single pass. However, such a single pass broadcast approach is not realistic in many practical implementations. For example, under the popular parameter server model for distributed learning, the worker nodes need to send the compressed local gradients to the parameter server, which performs the aggregation. The parameter server has to compress the aggregated stochastic gradient again before sending it back to the worker nodes. In this work, we provide a detailed analysis on this two-pass communication model and its asynchronous parallel variant, with error-compensated compression both on the worker nodes and on the parameter server. We show that the error-compensated stochastic gradient algorithm admits three very nice properties: 1) it is compatible with an \emph{arbitrary} compression technique; 2) it admits an improved convergence rate than the non error-compensated stochastic gradient methods such as QSGD and sparse SGD; 3) it admits linear speedup with respect to the number of workers. The empirical study is also conducted to validate our theoretical results.

Connected through hyperedge HE-1 (induced by source paper "gradient sparification for asynchronous distributed training"):
  with members:
    - "sparcml high performance sparse communication for machine learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "stochastic nonconvex optimization with large minibatches"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "randomized reactive redundancy for byzantine fault tolerance in parallelized learning"):
  with members:
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "byzantine fault tolerant distributed linear regression"
    - "fast machine learning with byzantine workers and servers"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "sparse communication for distributed gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

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.DC(Distributed, Parallel, and Cluster Computing)