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: "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"
  Abstract: Recent technological advances have proliferated the available computing power, memory, and speed of modern Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning. While GPU-accelerated Deep Neural Networks (DNNs) currently offer state-of-the-art performance, they consume large amounts of power. Training such networks on CPUs is inefficient, as data throughput and parallel computation is limited. FPGAs are considered a suitable candidate for performance critical, low power systems, e.g. the Internet of Things (IOT) edge devices. Using the Xilinx SDAccel or Intel FPGA SDK for OpenCL development environment, networks described using the high-level OpenCL framework can be accelerated on heterogeneous platforms. Moreover, the resource utilization and power consumption of DNNs can be further enhanced by utilizing regularization techniques that binarize network weights. In this paper, we introduce, to the best of our knowledge, the first FPGA-accelerated stochastically binarized DNN implementations, and compare them to implementations accelerated on both GPUs and FPGAs. All our developed networks are trained and benchmarked using the popular MNIST and CIFAR-10 datasets. For our binarized and conventional FPGA-based networks, we achieve a >16-fold improvement in power consumption, compared to their GPU-accelerated counterparts. Also, our binarized FPGA-based networks require >25% shorter inference times, compared to their GPU-based counterparts.

Connected through hyperedge HE-1 (induced by source paper "training progressively binarizing deep networks using fpgas"):
  with members:
    - "self binarizing networks"
    - "towards binary valued gates for robust lstm training"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "bnn improved binary network training"
    - "towards accurate binary convolutional neural network"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "the convolutional tsetlin machine"):
  with members:
    - "a scheme for continuous input to the tsetlin machine with applications to forecasting disease outbreaks"
    - "learning logistic circuits"
    - "using the tsetlin machine to learn human interpretable rules for high accuracy text categorization with medical applications"
    - "deep learning for classical japanese literature"
    - "the tsetlin machine a game theoretic bandit driven approach to optimal pattern recognition with propositional logic"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "tune a research platform for distributed model selection and training"
    - "bnn improved binary network training"

Connected through hyperedge HE-3 (induced by source paper "variation aware binarized memristive networks"):
  with members:
    - "binary weighted memristive analog deep neural network for near sensor edge processing"

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)