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: "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
  Abstract: Binary neural networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR neural engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM/standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65- and 22-nm technology for the XNE IP and post-layout results in 22 nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6 fJ per operation at 0.4 V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2 mJ per frame at 8.9 frames/s.

Connected through hyperedge HE-1 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"
    - "haq hardware aware automated quantization with mixed precision"
    - "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"
    - "pact parameterized clipping activation for quantized neural networks"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "minimum energy quantized neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-2 (induced by source paper "synaptic metaplasticity in binarized neural networks"):
  with members:
    - "memory aware synapses learning what not to forget"
    - "continual learning with deep generative replay"
    - "towards accurate binary convolutional neural network"
    - "continual learning through synaptic intelligence"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "icarl incremental classifier and representation learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "optimally scheduling cnn convolutions for efficient memory access"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "a systematic approach to blocking convolutional neural networks"
    - "ristretto hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "pimball binary neural networks in spintronic memory"):
  with members:
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "energy efficient hybrid stochastic binary neural networks for near sensor computing"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-5 (induced by source paper "ppac a versatile in memory accelerator for matrix vector product like operations"):
  with members:
    - "a microprocessor implemented in 65nm cmos with configurable and bit scalable accelerator for programmable in memory computing"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-6 (induced by source paper "stream semantic registers a lightweight risc v isa extension achieving full compute utilization in single issue cores"):
  with members:
    - "ara a 1 ghz scalable and energy efficient risc v vector processor with multi precision floating point support in 22 nm fd soi"
    - "the cost of application class processing energy and performance analysis of a linux ready 1 7 ghz 64 bit risc v core in 22 nm fdsoi technology"
    - "a scalable near memory architecture for training deep neural networks on large in memory datasets"

Connected through hyperedge HE-7 (induced by source paper "a machine learning accelerator in memory for energy harvesting"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-8 (induced by source paper "pbgen partial binarization of deconvolution based generators for edge intelligence"):
  with members:
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "pca driven hybrid network design for enabling intelligence at the edge"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "binary generative adversarial networks for image retrieval"

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.NE(Neural and Evolutionary Computing)