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: "evolution of plastic learning in spiking networks via memristive connections"
  Abstract: This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks.

Connected through hyperedge HE-1 (induced by source paper "crossing the reality gap with evolved plastic neurocontrollers"):
  with members:
    - "evolving spiking neural networks for nonlinear control problems"

Connected through hyperedge HE-2 (induced by source paper "parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "rank pooling for action recognition"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "the treasure beneath convolutional layers cross convolutional layer pooling for image classification"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "born to learn the inspiration progress and future of evolved plastic artificial neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "evolving deep neural networks"
    - "lstm a search space odyssey"
    - "progressive neural networks"
    - "hierarchical representations for efficient architecture search"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-4 (induced by source paper "evolving spiking neural networks for nonlinear control problems"):
  with members:
    - "information bottleneck in control tasks with recurrent spiking neural networks"
    - "a cognitive architecture based on a learning classifier system with spiking classifiers"

Connected through hyperedge HE-5 (induced by source paper "slime mould memristors"):
  with members:
    - "is spiking logic the route to memristor based computers"
    - "towards slime mould chemical sensor mapping chemical inputs onto electrical potential dynamics of physarum polycephalum"
    - "towards slime mould colour sensor recognition of colours by physarum polycephalum"
    - "slime mould tactile sensor"
    - "slime mould computes planar shapes"

Connected through hyperedge HE-6 (induced by source paper "evolving unipolar memristor spiking neural networks"):
  with members:
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-7 (induced by source paper "evolving spiking networks with variable resistive memories"):
  with members:
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-8 (induced by source paper "does the d c response of memristors allow robotic short term memory and a possible route to artificial time perception"):
  with members:
    - "beyond markov chains towards adaptive memristor network based music generation"

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)