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: "gated xnor networks deep neural networks with ternary weights and activations under a unified discretization framework"
  Abstract: There is a pressing need to build an architecture that could subsume these networks undera unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multistep neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. In this way, we build a unified framework that subsumes the binary or ternary networks as its special cases.More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to gated XNOR networks (or sparse binary networks) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore,the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.

Connected through hyperedge HE-1 (induced by source paper "table based neural units fully quantizing networks for multiply free inference"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "training and inference with integers in deep neural networks"
    - "no multiplication no floating point no problem training networks for efficient inference"
    - "a survey on methods and theories of quantized neural networks"
    - "training quantized nets a deeper understanding"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-2 (induced by source paper "binary ensemble neural network more bits per network or more networks per bit"):
  with members:
    - "model compression via distillation and quantization"
    - "towards fast and energy efficient binarized neural network inference on fpga"
    - "network sketching exploiting binary structure in deep cnns"
    - "the high dimensional geometry of binary neural networks"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "training quantized nets a deeper understanding"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-3 (induced by source paper "no multiplication no floating point no problem training networks for efficient inference"):
  with members:
    - "empirical explorations in training networks with discrete activations"
    - "training and inference with integers in deep neural networks"
    - "conditional probability models for deep image compression"
    - "training quantized nets a deeper understanding"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "full resolution image compression with recurrent neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "compression artifacts removal using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "constrained image generation using binarized neural networks with decision procedures"):
  with members:
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "gans for biological image synthesis"
    - "maximum resilience of artificial neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safety verification of deep neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)