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: "an ultra low power sigma delta neuron circuit"
  Abstract: Neural processing systems typically represent data using leaky integrate and fire (LIF) neuron models that generate spikes or pulse trains at a rate proportional to their input amplitudes. This mechanism requires high firing rates when encoding time-varying signals, leading to increased power consumption. Neuromorphic systems that use adaptive LIF neuron models overcome this problem by encoding signals in the relative timing of their output spikes rather than their rate. In this paper, we analyze recent adaptive LIF neuron circuit implementations and highlight the analogies and differences between them and a first-order sigma-delta feedback loop. We propose a new sigma-delta neuron circuit that addresses some of the limitations in existing implementations and present simulation results that quantify the improvements. We show that the new circuit, implemented in a 1.8 V, 180 nm CMOS process, offers up to 42 dB signal-to-distortion ratio and consumes orders of magnitude lower energy. Finally, we also demonstrate how the sigma-delta interpretation enables mapping of real-valued recurrent neural network to the spiking framework to emphasize the envisioned application of the proposed circuit.

Connected through hyperedge HE-1 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips"):
  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"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-2 (induced by source paper "a neuromorphic boost to rnns using low pass filters"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "origami a 803 gop s w convolutional network accelerator"

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)