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: "superconducting optoelectronic circuits for neuromorphic computing"
  Abstract: Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least $20$ MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm$^2$ for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of $\approx 20$ aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes $>$ 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

Connected through hyperedge HE-1 (induced by source paper "fluxonic processing of photonic synapse events"):
  with members:
    - "the largest cognitive systems will be optoelectronic"
    - "circuit designs for superconducting optoelectronic loop neurons"
    - "toward fast neural computing using all photonic phase change spiking neurons"

Connected through hyperedge HE-2 (induced by source paper "in memory computing on a photonic platform"):
  with members:
    - "mixed precision in memory computing"
    - "temporal correlation detection using computational phase change memory"
    - "mixed precision memcomputing"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"
    - "universal memcomputing machines"
    - "the parallel approach"

Connected through hyperedge HE-3 (induced by source paper "the largest cognitive systems will be optoelectronic"):
  with members:
    - "superconducting optoelectronic neurons i general principles"
    - "superconducting optoelectronic neurons iii synaptic plasticity"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-4 (induced by source paper "circuit designs for superconducting optoelectronic loop neurons"):
  with members:
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "principles of neuromorphic photonics"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "representation learning a review and new perspectives"
    - "optoelectronic reservoir computing"

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)