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 "binary ensemble neural network more bits per network or more networks per bit"):
  Abstract: Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose weights and activations are both single bits suffer from severe accuracy degradation. To understand why, we investigate the representation ability, speed and bias/variance of BNNs through extensive experiments. We conclude that the error of BNNs is predominantly caused by the intrinsic instability (training time) and non-robustness (train & test time). Inspired by this investigation, we propose the Binary Ensemble Neural Network (BENN) which leverages ensemble methods to improve the performance of BNNs with limited efficiency cost. While ensemble techniques have been broadly believed to be only marginally helpful for strong classifiers such as deep neural networks, our analyses and experiments show that they are naturally a perfect fit to boost BNNs. We find that our BENN, which is faster and much more robust than state-of-the-art binary networks, can even surpass the accuracy of the full-precision floating number network with the same architecture.

Includes members:
  Member "model compression via distillation and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "towards fast and energy efficient binarized neural network inference on fpga":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting processing in non volatile memory for binary neural network accelerators")
  Member "network sketching exploiting binary structure in deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic pruning for quantized neural networks")
  Member "gated xnor networks deep neural networks with ternary weights and activations under a unified discretization framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "table based neural units fully quantizing networks for multiply free inference")
  Member "the high dimensional geometry of binary neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "incremental boosting convolutional neural network for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust regression via deep negative correlation learning")
  Member "training quantized nets a deeper understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "leanconvnets low cost yet effective convolutional neural networks")
  Member "towards accurate binary convolutional neural network":
    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)