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: "quantized decentralized consensus optimization"
  Abstract: We consider the problem of decentralized consensus optimization, where the sum of $n$ convex functions are minimized over $n$ distributed agents that form a connected network. In particular, we consider the case that the communicated local decision variables among nodes are quantized in order to alleviate the communication bottleneck in distributed optimization. We propose the Quantized Decentralized Gradient Descent (QDGD) algorithm, in which nodes update their local decision variables by combining the quantized information received from their neighbors with their local information. We prove that under standard strong convexity and smoothness assumptions for the objective function, QDGD achieves a vanishing mean solution error. To the best of our knowledge, this is the first algorithm that achieves vanishing consensus error in the presence of quantization noise. Moreover, we provide simulation results that show tight agreement between our derived theoretical convergence rate and the experimental results.

Connected through hyperedge HE-1 (induced by source paper "decentralized deep learning with arbitrary communication compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "robust and communication efficient collaborative learning"
    - "an exact quantized decentralized gradient descent algorithm"
    - "texttt deepsqueeze decentralization meets error compensated compression"

Connected through hyperedge HE-2 (induced by source paper "moniqua modulo quantized communication in decentralized sgd"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "asynchronous decentralized optimization in directed networks"
    - "distributed learning with sublinear communication"
    - "texttt deepsqueeze decentralization meets error compensated compression"
    - "communication compression for decentralized training"
    - "high accuracy low precision training"
    - "distributed learning over unreliable networks"
    - "sparsified sgd with memory"

Connected through hyperedge HE-3 (induced by source paper "distributed fixed point methods with compressed iterates"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "natural compression for distributed deep learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "gradient descent with compressed iterates"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"

Connected through hyperedge HE-4 (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"
    - "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"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-5 (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"
    - "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"
    - "fast distributed gradient methods"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-6 (induced by source paper "communication efficient network distributed optimization with differential coded compressors"):
  with members:
    - "robust and communication efficient collaborative learning"
    - "communication compression for decentralized training"
    - "compressed distributed gradient descent communication efficient consensus over networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-7 (induced by source paper "compressed distributed gradient descent communication efficient consensus over networks"):
  with members:
    - "communication compression for decentralized training"
    - "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"
    - "decentralization meets quantization"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

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.LG(Machine Learning)