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: "resistive threshold logic"
  Abstract: We report a resistance-based threshold logic family useful for mimicking brain-like large variable logic functions in VLSI. A universal boolean logic cell based on an analog resistive divider and threshold logic circuit is presented. The resistive divider is implemented using memristors, and provides output voltage as a summation of weighted product of input voltages. The output of the resistive divider is converted into a binary value by a threshold operation implemented by CMOS inverter and/or Opamp. A universal cell structure is presented to decrease the overall implementation complexity and number of components. When the number of input variables becomes very high, the proposed cell offers advantages of smaller area and design simplicity in comparison with CMOS-based logic circuits.

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"
    - "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"
    - "memristive lstm network hardware architecture for time series predictive modeling problem"
    - "approximate probabilistic neural networks with gated threshold logic"

Connected through hyperedge HE-2 (induced by source paper "practical implementation of memristor based threshold logic gates"):
  with members:
    - "programmable memristive threshold logic gate array"
    - "mixed precision in memory computing"
    - "hierarchical temporal memory using memristor networks a survey"
    - "approximate probabilistic neural networks with gated threshold logic"
    - "a survey of memristive threshold logic circuits"
    - "memristor threshold logic an overview to challenges and applications"
    - "a reconfigurable fir filter with memristor based weights"
    - "voltage controlled memristor threshold logic gates"

Connected through hyperedge HE-3 (induced by source paper "a survey of memristive threshold logic circuits"):
  with members:
    - "memristive threshold logic circuit design of fast moving object detection"
    - "threshold logic computing memristive cmos circuits for fast fourier transform and vedic multiplication"
    - "design and synthesis of ultralow energy spin memristor threshold logic"
    - "ultra low energy high performance dynamic resistive threshold logic"
    - "cognitive memory network"

Connected through hyperedge HE-4 (induced by source paper "memristive system design for variable pixel g neighbor denoising filter"):
  with members:
    - "a survey of memristive threshold logic circuits"
    - "voltage controlled memristor threshold logic gates"
    - "threshold logic computing memristive cmos circuits for fast fourier transform and vedic multiplication"
    - "a biomimetic model of the outer plexiform layer by incorporating memristive devices"

Connected through hyperedge HE-5 (induced by source paper "voltage controlled memristor threshold logic gates"):
  with members:
    - "memristive threshold logic circuit design of fast moving object detection"
    - "threshold logic computing memristive cmos circuits for fast fourier transform and vedic multiplication"

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)