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: "fast distributed gradient methods"
  Abstract: We study distributed optimization problems when $N$ nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant $L$), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node communications $\mathcal{K}$ and the per-node gradient evaluations $k$. Our first method, Distributed Nesterov Gradient, achieves rates $O\left({\log \mathcal{K}}/{\mathcal{K}}\right)$ and $O\left({\log k}/{k}\right)$. Our second method, Distributed Nesterov gradient with Consensus iterations, assumes at all nodes knowledge of $L$ and $\mu(W)$ -- the second largest singular value of the $N \times N$ doubly stochastic weight matrix $W$. It achieves rates $O\left({1}/{\mathcal{K}^{2-\xi}}\right)$ and $O\left({1}/{k^2}\right)$ ($\xi>0$ arbitrarily small). Further, we give with both methods explicit dependence of the convergence constants on $N$ and $W$. Simulation examples illustrate our findings.

Connected through hyperedge HE-1 (induced by source paper "on local computation for optimization in multi agent systems"):
  with members:
    - "fast local computation algorithms"
    - "local computation lower and upper bounds"

Connected through hyperedge HE-2 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "communication efficient massive uav online path control federated learning meets mean field game theory"
    - "predictive control and communication co design a gaussian process regression approach"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "accelerating federated learning via momentum gradient descent"
    - "advances and open problems in federated learning"
    - "gadmm fast and communication efficient framework for distributed machine learning"

Connected through hyperedge HE-3 (induced by source paper "robust and communication efficient collaborative learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "an exact quantized decentralized gradient descent algorithm"
    - "speeding up distributed machine learning using codes"
    - "quantized decentralized consensus optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "compressed distributed gradient descent communication efficient consensus over networks"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "gadmm fast and communication efficient framework for distributed machine learning"):
  with members:
    - "wireless network intelligence at the edge"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "proximity without consensus in online multiagent optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed mean estimation with limited communication"
    - "distributed optimization with arbitrary local solvers"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-5 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed k means and k median clustering on general topologies"
    - "distributed strongly convex optimization"
    - "scalable k means"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "decentralized stochastic optimization and gossip algorithms with compressed communication"):
  with members:
    - "communication compression for decentralized training"
    - "stochastic gradient push for distributed deep learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "an exact quantized decentralized gradient descent algorithm"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded computing for distributed graph analytics"
    - "quantized decentralized consensus optimization"
    - "communication vs distributed computation an alternative trade off curve"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-8 (induced by source paper "distributed nesterov gradient methods over arbitrary graphs"):
  with members:
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "average consensus on general strongly connected digraphs"

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.IT(Information Theory)