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: "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
  Abstract: Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have been shown to be effective in special-purpose accelerators for a limited set of neural network applications. #R##N#We present the Programmable Ultra-efficient Memristor-based Accelerator (PUMA) which enhances memristor crossbars with general purpose execution units to enable the acceleration of a wide variety of Machine Learning (ML) inference workloads. PUMA's microarchitecture techniques exposed through a specialized Instruction Set Architecture (ISA) retain the efficiency of in-memory computing and analog circuitry, without compromising programmability. #R##N#We also present the PUMA compiler which translates high-level code to PUMA ISA. The compiler partitions the computational graph and optimizes instruction scheduling and register allocation to generate code for large and complex workloads to run on thousands of spatial cores. #R##N#We have developed a detailed architecture simulator that incorporates the functionality, timing, and power models of PUMA's components to evaluate performance and energy consumption. A PUMA accelerator running at 1 GHz can reach area and power efficiency of $577~GOPS/s/mm^2$ and $837~GOPS/s/W$, respectively. Our evaluation of diverse ML applications from image recognition, machine translation, and language modelling (5M-800M synapses) shows that PUMA achieves up to $2,446\times$ energy and $66\times$ latency improvement for inference compared to state-of-the-art GPUs. Compared to an application-specific memristor-based accelerator, PUMA incurs small energy overheads at similar inference latency and added programmability.

Connected through hyperedge HE-1 (induced by source paper "compiling neural networks for a computational memory accelerator"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "a fast analytical model of fully associative caches"
    - "automated derivation of parametric data movement lower bounds for affine programs"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"

Connected through hyperedge HE-2 (induced by source paper "exploiting oxide based resistive ram variability for bayesian neural network hardware design"):
  with members:
    - "all spin bayesian neural networks"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-3 (induced by source paper "geniex a generalized approach to emulating non ideality in memristive xbars using neural networks"):
  with members:
    - "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"

Connected through hyperedge HE-4 (induced by source paper "3d acortex an ultra compact energy efficient neurocomputing platform based on commercial 3d nand flash memories"):
  with members:
    - "a behavioral compact model for static characteristics of 3d nand flash memory"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "rapidnn in memory deep neural network acceleration framework"
    - "deep learning for iot big data and streaming analytics a survey"
    - "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-5 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"

Connected through hyperedge HE-6 (induced by source paper "tim dnn ternary in memory accelerator for deep neural networks"):
  with members:
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "ternary neural networks with fine grained quantization"
    - "computing in memory with spin transfer torque magnetic ram"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "effective quantization methods for recurrent neural networks"
    - "trained ternary quantization"

Connected through hyperedge HE-7 (induced by source paper "panther a programmable architecture for neural network training harnessing energy efficient reram"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "training and inference with integers in deep neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "training deep neural networks with 8 bit floating point numbers"
    - "revisiting small batch training for deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mixed precision training"

Connected through hyperedge HE-8 (induced by source paper "all spin bayesian neural networks"):
  with members:
    - "implementing bayesian networks with embedded stochastic mram"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "vime variational information maximizing exploration"

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)