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: "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"
  Abstract: Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the initialization stage of the HTM. We propose the alternative deterministic method for the HTM initialization stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.

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 "hierarchical temporal memory using memristor networks a survey"):
  with members:
    - "on chip face recognition system design with memristive hierarchical temporal memory"
    - "a survey of memristive threshold logic circuits"
    - "hierarchical temporal memory based on spin neurons and resistive memory for energy efficient brain inspired computing"
    - "non volatile hierarchical temporal memory hardware for spatial pooling"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-3 (induced by source paper "binary weighted memristive analog deep neural network for near sensor edge processing"):
  with members:
    - "hierarchical temporal memory using memristor networks a survey"
    - "hierarchical temporal memory based on spin neurons and resistive memory for energy efficient brain inspired computing"

Connected through hyperedge HE-4 (induced by source paper "analog multiplier design with cmos memristor circuits"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"
    - "memristor load current mirror circuit"

Connected through hyperedge HE-5 (induced by source paper "sense amplifier design using cmos memristor circuits"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"
    - "memristor load current mirror circuit"

Connected through hyperedge HE-6 (induced by source paper "analysis of multilayer perceptron with rectifier linear unit activation function"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"

Connected through hyperedge HE-7 (induced by source paper "variability analysis of memristor based sigmoid function"):
  with members:
    - "neuron inspired data encoding memristive multi level memory cell"

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)