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: "mixed precision in memory computing"
  Abstract: As complementary metal–oxide–semiconductor (CMOS) scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to extend the performance of today’s computers substantially. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a computational memory unit, are used for both processing and memory. However, to reach the numerical accuracy typically required for data analytics and scientific computing, limitations arising from device variability and non-ideal device characteristics need to be addressed. Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to iteratively improve the accuracy of the solution. The system therefore benefits from both the high precision of digital computing and the energy/areal efficiency of in-memory computing. We experimentally demonstrate the efficacy of the approach by accurately solving systems of linear equations, in particular, a system of 5,000 equations using 998,752 phase-change memory devices.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"

Connected through hyperedge HE-3 (induced by source paper "accurate deep neural network inference using computational phase change memory"):
  with members:
    - "improving noise tolerance of mixed signal neural networks"
    - "dissecting the nvidia turing t4 gpu via microbenchmarking"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "in datacenter performance analysis of a tensor processing unit"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-4 (induced by source paper "long short term memory networks in memristor crossbar arrays"):
  with members:
    - "approximate fpga based lstms under computation time constraints"
    - "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
    - "the microsoft 2017 conversational speech recognition system"
    - "learning to detect violent videos using convolutional long short term memory"
    - "data recombination for neural semantic parsing"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-5 (induced by source paper "an overview of in memory processing with emerging non volatile memory for data intensive applications"):
  with members:
    - "red a reram based deconvolution accelerator"
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "temporal correlation detection using computational phase change memory"
    - "computing in memory with spin transfer torque magnetic ram"
    - "graphr accelerating graph processing using reram"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "harnessing intrinsic noise in memristor hopfield neural networks for combinatorial optimization"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "fault tolerant dot product engines"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "part detector discovery in deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "5 parallel prism a topology for pipelined implementations of convolutional neural networks using computational memory"):
  with members:
    - "training deep neural networks with 8 bit floating point numbers"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-8 (induced by source paper "practical implementation of memristor based threshold logic gates"):
  with members:
    - "programmable memristive threshold logic gate array"
    - "hierarchical temporal memory using memristor networks a survey"
    - "approximate probabilistic neural networks with gated threshold logic"
    - "a survey of memristive threshold logic circuits"
    - "memristor threshold logic an overview to challenges and applications"
    - "a reconfigurable fir filter with memristor based weights"
    - "voltage controlled memristor threshold logic gates"
    - "memristive threshold logic circuit design of fast moving object detection"

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.ET(Emerging Technologies)