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: "neuron inspired data encoding memristive multi level memory cell"
  Abstract: Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory (MLM) cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory.

Connected through hyperedge HE-1 (induced by source paper "neuromemristive circuits for edge computing a review"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "learning in memristive neural network architectures using analog backpropagation circuits"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "memristive lstm network hardware architecture for time series predictive modeling problem"
    - "approximate probabilistic neural networks with gated threshold logic"
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"

Connected through hyperedge HE-2 (induced by source paper "learning in memristive neural network architectures using analog backpropagation circuits"):
  with members:
    - "neuromemristive circuits for edge computing a review"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "lstm a search space odyssey"
    - "on chip face recognition system design with memristive hierarchical temporal memory"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "multi level memristive memory with resistive networks"

Connected through hyperedge HE-3 (induced by source paper "an overview of memristive cryptography"):
  with members:
    - "neuromemristive circuits for edge computing a review"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "an analysis framework for hardware and software implementations with applications from cryptography"

Connected through hyperedge HE-4 (induced by source paper "analog multiplier design with cmos memristor circuits"):
  with members:
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"
    - "memristor load current mirror circuit"

Connected through hyperedge HE-5 (induced by source paper "sense amplifier design using cmos memristor circuits"):
  with members:
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"
    - "memristor load current mirror circuit"

Connected through hyperedge HE-6 (induced by source paper "fuzzy membership function implementation with memristor"):
  with members:
    - "memristor model based on fuzzy window function"
    - "neuro fuzzy computing system with the capacity of implementation on memristor crossbar and optimization free hardware training"

Connected through hyperedge HE-7 (induced by source paper "analysis of multilayer perceptron with rectifier linear unit activation function"):
  with members:
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"

Connected through hyperedge HE-8 (induced by source paper "implementation of chua s chaotic oscillator with an hp memristor"):
  with members:
    - "multi level memristive memory with resistive networks"

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)