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: "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
  Abstract: This paper introduces BinarEye: a digital processor for always-on Binary Convolutional Neural Networks. The chip maximizes data reuse through a Neuron Array exploiting local weight Flip-Flops. It stores full network models and feature maps and hence requires no off-chip bandwidth, which leads to a 230 1b-TOPS/W peak efficiency. Its 3 levels of flexibility - (a) weight reconfiguration, (b) a programmable network depth and (c) a programmable network width - allow trading energy for accuracy depending on the task's requirements. BinarEye's full system input-to-label energy consumption ranges from 14.4uJ/f for 86% CIFAR-10 and 98% owner recognition down to 0.92uJ/f for 94% face detection at up to 1700 frames per second. This is 3-12-70x more efficient than the state-of-the-art at on-par accuracy.

Connected through hyperedge HE-1 (induced by source paper "precise neural network computation with imprecise analog devices"):
  with members:
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "memory and information processing in neuromorphic systems"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "adam a method for stochastic optimization"
    - "low precision arithmetic for deep learning"

Connected through hyperedge HE-2 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "toward computation and memory efficient neural network acoustic models with binary weights and activations"

Connected through hyperedge HE-3 (induced by source paper "single bit per weight deep convolutional neural networks without batch normalization layers for embedded systems"):
  with members:
    - "group normalization"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "on calibration of modern neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "sharp minima can generalize for deep nets"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "improved regularization of convolutional neural networks with cutout"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-4 (induced by source paper "morphic a 65 nm 738k synapse mm 2 quad core binary weight digital neuromorphic processor with stochastic spike driven online learning"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "minimum energy quantized neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

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.DC(Distributed, Parallel, and Cluster Computing)