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: "training dnns with hybrid block floating point"
  Abstract: The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full precision floating-point arithmetic to maximize performance per area. Ongoing research efforts seek to further increase that performance density by replacing floating-point with fixed-point arithmetic. However, a significant roadblock for these attempts has been fixed point's narrow dynamic range, which is insufficient for DNN training convergence. We identify block floating point (BFP) as a promising alternative representation since it exhibits wide dynamic range and enables the majority of DNN operations to be performed with fixed-point logic. Unfortunately, BFP alone introduces several limitations that preclude its direct applicability. In this work, we introduce HBFP, a hybrid BFP-FP approach, which performs all dot products in BFP and other operations in floating point. HBFP delivers the best of both worlds: the high accuracy of floating point at the superior hardware density of fixed point. For a wide variety of models, we show that HBFP matches floating point's accuracy while enabling hardware implementations that deliver up to 8.5x higher throughput.

Connected through hyperedge HE-1 (induced by source paper "boosted and differentially private ensembles of decision trees"):
  with members:
    - "differentially private learning of geometric concepts"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "differentially private fair learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "differentially private random decision forests using smooth sensitivity"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "dsconv efficient convolution operator"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"

Connected through hyperedge HE-3 (induced by source paper "distributed learning over unreliable networks"):
  with members:
    - "byzantine stochastic gradient descent"
    - "communication compression for decentralized training"
    - "sparcml high performance sparse communication for machine learning"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cola decentralized linear learning"

Connected through hyperedge HE-4 (induced by source paper "communication compression for decentralized training"):
  with members:
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "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"
    - "randomized distributed mean estimation accuracy vs communication"
    - "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)