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: "signsgd with majority vote is communication efficient and fault tolerant"
  Abstract: Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults. We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD. Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote. This algorithm uses $32\times$ less communication per iteration than full-precision, distributed SGD. Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct. Aggregating sign gradients by majority vote means that no individual worker has too much power. We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially. The class of adversaries we consider includes as special cases those that invert or randomise their gradient estimate. On the practical side, we built our distributed training system in Pytorch. Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.

Connected through hyperedge HE-1 (induced by source paper "threats to federated learning a survey"):
  with members:
    - "fedvision an online visual object detection platform powered by federated learning"
    - "idlg improved deep leakage from gradients"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-2 (induced by source paper "uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "distributed momentum for byzantine resilient learning"):
  with members:
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "can you really backdoor federated learning"
    - "sgd decentralized byzantine resilience"
    - "momentum based variance reduction in non convex sgd"
    - "a little is enough circumventing defenses for distributed learning"
    - "byzantine stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

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"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "variance based gradient compression for efficient distributed deep learning"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "how to backdoor federated learning"

Connected through hyperedge HE-5 (induced by source paper "slowmo improving communication efficient distributed sgd with slow momentum"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "understanding the role of momentum in stochastic gradient methods"
    - "lookahead optimizer k steps forward 1 step back"
    - "roberta a robustly optimized bert pretraining approach"
    - "decentralized deep learning with arbitrary communication compression"

Connected through hyperedge HE-6 (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"
    - "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"
    - "hierarchical coding for distributed computing"

Connected through hyperedge HE-7 (induced by source paper "communication efficient distributed sgd with sketching"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (induced by source paper "variance reduced local sgd with lower communication complexity"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "federated learning challenges methods and future directions"
    - "faster distributed deep net training computation and communication decoupled stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"

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)