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: "memcomputing with membrane memcapacitive systems"
  Abstract: We show theoretically that networks of membrane memcapacitive systems—capacitors with memory made out of membrane materials—can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network. This polymorphism is an important characteristic of memcomputing (computing with memories) that closely reproduces one of the main features of the brain. A practical realization of these membrane memcapacitive systems, using, e.g., graphene or other 2D materials, would be a step forward towards a solid-state realization of memcomputing with passive devices.

Connected through hyperedge HE-1 (induced by source paper "a demonstration of implication logic based on volatile diffusive memristors"):
  with members:
    - "dynamic computing random access memory"

Connected through hyperedge HE-2 (induced by source paper "perspective memcomputing leveraging memory and physics to compute efficiently"):
  with members:
    - "instantons in self organizing logic gates"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "universal memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "dynamic computing random access memory"
    - "the parallel approach"
    - "self organization and solution of shortest path optimization problems with memristive networks"

Connected through hyperedge HE-3 (induced by source paper "memcapacitive devices in logic and crossbar applications"):
  with members:
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "dynamic computing random access memory"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"
    - "memristors can implement fuzzy logic"

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)