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: "neuro fuzzy computing system with the capacity of implementation on memristor crossbar and optimization free hardware training"
  Abstract: Abstract—In this paper, ﬁrst we present a new explanationfor the relation between logical circuits and artiﬁcial neu ralnetworks, logical circuits and fuzzy logic, and artiﬁcial n euralnetworks and fuzzy inference systems. Then, based on theseresults, we propose a new neuro-fuzzy computing system whichcan effectively be implemented on the memristor-crossbar struc-ture. One important feature of the proposed system is that itshardware can directly be trained using the Hebbian learning ruleand without the need to any optimization. The system also hasa very good capability to deal with huge number of input-outtraining data without facing problems like overtraining.Index Terms—Logical circuit, Fuzzy logic, Neural network,Neuro-fuzzy computing system, Memristive device, MemristorCrossbar, Hebbian Learning Rule. I. I NTRODUCTION During past years, lots of efforts have been made to ap-proach to the computing power of human brain. These effortsroughly can be categorized into several different areas suchas Artiﬁcial Neural Networks (ANNs), Fuzzy Logic, etc. Byreviewing these works we can simply recognize that mostof them have only concentrated on the software and we canrarely ﬁnd a good sample for hardware implementation of anintelligent system. In addition, lots of the suggested structuresor methods do not have biological support. By consideringthe number of neurons in the human brain and the complexityof connections between them, the importance of having anefﬁcient hardware with the ability of expanding into that sc alebecomes more and more clear. According to the nature ofcomputation and memory in brain, now it is a well-acceptedfact that this hardware should be in analog form since inthis case it can work much faster than conventional digitalcircuits. However, heretofore, there was a big obstacle in frontof reaching this goal. In fact, there was no simple passiveelement that can be used for storing and manipulation of datalike synaptic weights. Note that although analog values canbe stored in capacitors as voltage or charge, the stored valuescannot easily be read and used in computations without beingaltered. In addition, according to the leakage problem the datastored in capacitors will vary in time. As a result, most of theanalog hardwares proposed so far are somehow inefﬁcient andarea consuming designs (see, for example [1], [2], [3]).

Connected through hyperedge HE-1 (induced by source paper "fuzzy membership function implementation with memristor"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"
    - "memristor model based on fuzzy window function"

Connected through hyperedge HE-2 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"

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.NE(Neural and Evolutionary Computing)