human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "practical implementation of memristor based threshold logic gates"):
  Abstract: Current advances in emerging memory technologies enable novel and unconventional computing architectures for high-performance and low-power electronic systems, capable of carrying out massively parallel operations at the edge. One emerging technology, ReRAM, also known to belong in the family of memristors (memory resistors), is gathering attention due to its attractive features for logic and in-memory computing; benefits which follow from its technological attributes, such as nanoscale dimensions, low power operation and multi-state programming. At the same time, design with CMOS is quickly reaching its physical and functional limitations, and further research towards novel logic families, such as Threshold Logic Gates (TLGs) is scoped. TLGs constitute a logic family known for its high-speed and low power consumption, yet rely on conventional transistor technology. Introducing memristors enables a more affordable reconfiguration capability of TLGs. Through this work, we are introducing a physical implementation of a memristor-based current-mode TLG (MCMTLG) circuit and validate its design and operation through multiple experimental setups. We demonstrate 2-input and 3-input MCMTLG configurations and showcase their reconfiguration capability. This is achieved by varying memristive weights arbitrarily for shaping the classification decision boundary, thus showing promise as an alternative hardware-friendly implementation of Artificial Neural Networks (ANNs). Through the employment of real memristor devices as the equivalent of synaptic weights in TLGs, we are realizing components that can be used towards an in-silico classifier.

Includes members:
  Member "programmable memristive threshold logic gate array":
    also connected through other hyperedges:
      (none)
  Member "mixed precision in memory computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "hierarchical temporal memory using memristor networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "approximate probabilistic neural networks with gated threshold logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "a survey of memristive threshold logic circuits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "memristor threshold logic an overview to challenges and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "a reconfigurable fir filter with memristor based weights":
    also connected through other hyperedges:
      (none)
  Member "voltage controlled memristor threshold logic gates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "programmable memristive threshold logic gate array")

Question: Please tell me which class the source paper 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)