human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization"):
  Abstract: We present Rotated Adaptive Tetra-iterated Quantizer (RATQ), a fixed-length quantizer for gradients in first order stochastic optimization. RATQ is easy to implement and involves only a Hadamard transform computation and adaptive uniform quantization with appropriately chosen dynamic ranges. For noisy gradients with almost surely bounded Euclidean norms, we establish an information theoretic lower bound for optimization accuracy using finite precision gradients and show that RATQ almost attains this lower bound. #R##N#For mean square bounded noisy gradients, we use a gain-shape quantizer which separately quantizes the Euclidean norm and uses RATQ to quantize the normalized unit norm vector. We establish lower bounds for performance of any optimization procedure and shape quantizer, when used with a uniform gain quantizer. Finally, we propose an adaptive quantizer for gain which when used with RATQ for shape quantizer outperforms uniform gain quantization and is, in fact, close to optimal. #R##N#As a by-product, we show that our fixed-length quantizer RATQ has almost the same performance as the optimal variable-length quantizers for distributed mean estimation. Also, we obtain an efficient quantizer for Gaussian vectors which attains a rate very close to the Gaussian rate-distortion function and is, in fact, universal for subgaussian input vectors.

Includes members:
  Member "error feedback fixes signsgd and other gradient compression schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "limits on gradient compression for stochastic optimization")
  Member "inference under information constraints ii communication constraints and shared randomness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints")
  Member "vqsgd vector quantized stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits on gradient compression for stochastic optimization")
  Member "distributed learning with sublinear communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limits on gradient compression for stochastic optimization")
  Member "open problem the oracle complexity of convex optimization with limited memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite precision stochastic optimization accounting for the bias")
  Member "geometric lower bounds for distributed parameter estimation under communication constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "sparsified sgd with memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

Question: Please tell me which class the source paper 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)