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: "randomized distributed mean estimation accuracy vs communication"
  Abstract: We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subproblem in many applications, including reduce-all operations within algorithms for distributed and federated optimization and learning. We propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. Our family contains the full-communication and zero-error method on one extreme, and an $\epsilon$-bit communication and ${\cal O}\left(1/(\epsilon n)\right)$ error method on the opposite extreme. In the special case where we communicate, in expectation, a single bit per coordinate of each vector, we improve upon existing results by obtaining $\mathcal{O}(r/n)$ error, where $r$ is the number of bits used to represent a floating point value.

Connected through hyperedge HE-1 (induced by source paper "update aware device scheduling for federated learning at the wireless edge"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "accelerating dnn training in wireless federated edge learning system"
    - "federated learning over wireless fading channels"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "age based scheduling policy for federated learning in mobile edge networks"
    - "scheduling policies for federated learning in wireless networks"

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"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-3 (induced by source paper "natural compression for distributed deep learning"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "distributed learning with compressed gradient differences"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated learning strategies for improving communication efficiency"
    - "not all samples are created equal deep learning with importance sampling"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-4 (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"
    - "variance based gradient compression for efficient distributed deep learning"
    - "don t use large mini batches use local sgd"

Connected through hyperedge HE-5 (induced by source paper "communication efficient distributed learning via lazily aggregated quantized gradients"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-6 (induced by source paper "distributed learning with compressed gradient differences"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "signsgd compressed optimisation for non convex problems"
    - "efficient distributed hessian free algorithm for large scale empirical risk minimization via accumulating sample strategy"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed optimization with arbitrary local solvers"

Connected through hyperedge HE-7 (induced by source paper "communication compression for decentralized training"):
  with members:
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "training dnns with hybrid block floating point"
    - "gradient sparsification for communication efficient distributed optimization"
    - "dynamic safe interruptibility for decentralized multi agent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "fully decentralized policies for multi agent systems an information theoretic approach"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-8 (induced by source paper "expanding the reach of federated learning by reducing client resource requirements"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "high accuracy low precision training"
    - "towards accurate binary convolutional neural network"
    - "emnist an extension of mnist to handwritten letters"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "distributed mean estimation with limited communication"
    - "federated optimization distributed machine learning for on device intelligence"

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)