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: "model compression as constrained optimization with application to neural nets part ii quantization"
  Abstract: We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal. The codebook can be optimally learned jointly with the net, or fixed, as for binarization or ternarization approaches. Previous work has quantized the weights of the reference net, or incorporated rounding operations in the backpropagation algorithm, but this has no guarantee of converging to a loss-optimal, quantized net. We describe a new approach based on the recently proposed framework of model compression as constrained optimization \citep{Carreir17a}. This results in a simple iterative "learning-compression" algorithm, which alternates a step that learns a net of continuous weights with a step that quantizes (or binarizes/ternarizes) the weights, and is guaranteed to converge to local optimum of the loss for quantized nets. We develop algorithms for an adaptive codebook or a (partially) fixed codebook. The latter includes binarization, ternarization, powers-of-two and other important particular cases. We show experimentally that we can achieve much higher compression rates than previous quantization work (even using just 1 bit per weight) with negligible loss degradation.

Connected through hyperedge HE-1 (induced by source paper "towards hardware implementation of neural network based communication algorithms"):
  with members:
    - "rethinking floating point for deep learning"
    - "building encoder and decoder with deep neural networks on the way to reality"
    - "an introduction to deep learning for the physical layer"
    - "training quantized nets a deeper understanding"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning with limited numerical precision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "proxquant quantized neural networks via proximal operators"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-4 (induced by source paper "binaryrelax a relaxation approach for training deep neural networks with quantized weights"):
  with members:
    - "stochastic layer wise precision in deep neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "sep nets small and effective pattern networks"

Connected through hyperedge HE-5 (induced by source paper "generalized ternary connect end to end learning and compression of multiplication free deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "wrpn apprentice methods for training and inference using low precision numerics"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "bitnet bit regularized deep neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "model compression as constrained optimization with application to neural nets part i general framework"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

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)