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: "multi level memristive memory with resistive networks"
  Abstract: Analog memory is of great importance in neurocomputing technologies field, but still remains difficult to implement. With emergence of memristors in VLSI technologies the idea of designing scalable analog data storage elements finds its second wind. A memristor, known for its history dependent resistance levels, independently can provide blocks of binary or discrete state data storage. However, using single memristor to save the analog value is practically limited due to the device variability and implementation complexity. In this paper, we present a new design of discrete state memory cell consisting of subcells constructed from a memristor and its resistive network. A memristor in the sub-cells provides the storage element, while its resistive network is used for programming its resistance. Several sub-cells are then connected in parallel, resembling potential divider configuration. The output of the memory cell is the voltage resulting from distributing the input voltage among the sub-cells. Here, proposed design was programmed to obtain 10 and 27 different output levels depending on the configuration of the combined resistive networks within the sub-cell. Despite the simplicity of the circuit, this realization of multilevel memory provides increased number of output levels compared to previous designs of memory technologies based on memristors. Simulation results of proposed memory are analyzed providing explicit data on the issues of distinguishing discrete analog output levels and sensitivity of the cell to oscillations in write signal patterns.

Connected through hyperedge HE-1 (induced by source paper "learning in memristive neural network architectures using analog backpropagation circuits"):
  with members:
    - "neuromemristive circuits for edge computing a review"
    - "neuron inspired data encoding memristive multi level memory cell"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "neuron inspired data encoding memristive multi level memory cell"):
  with members:
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-3 (induced by source paper "implementation of chua s chaotic oscillator with an hp memristor"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"

Connected through hyperedge HE-4 (induced by source paper "memristive lstm network hardware architecture for time series predictive modeling problem"):
  with members:
    - "design of cmos memristor circuits for lstm architecture"

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)