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: "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"
  Abstract: A multi-bit digital weight cell for high-performance, inference-only non-GPU-like neuromorphic accelerators is presented. The cell is designed with simplicity of peripheral circuitry in mind. Non-volatile storage of weights which eliminates the need for DRAM access is based on FeFETs and is purely digital. The Multiply-and-Accumulate operation is performed using passive resistors, gated by FeFETs. The resulting weight cell offers a high degree of linearity and a large ON/OFF ratio. The key performance tradeoffs are investigated, and the device requirements are elucidated.

Connected through hyperedge HE-1 (induced by source paper "a back end cmos compatible ferroelectric field effect transistor for synaptic weights"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"

Connected through hyperedge HE-2 (induced by source paper "high accuracy inference in neuromorphic circuits using hardware aware training"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "device and system level design considerations for analog non volatile memory based neuromorphic architectures"

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)