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: "binary weighted memristive analog deep neural network for near sensor edge processing"
  Abstract: The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%.

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"
    - "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"
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"

Connected through hyperedge HE-2 (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"
    - "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"
    - "multi level memristive memory with resistive networks"

Connected through hyperedge HE-3 (induced by source paper "variation aware binarized memristive networks"):
  with members:
    - "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"

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)