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: "a theoretical basis for efficient computations with noisy spiking neurons"
  Abstract: Network of neurons in the brain apply - unlike processors in our current generation of computer hardware - an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However it turned out to be surprisingly difficult to design networks of spiking neurons that are able to carry out demanding computations. We present here a new theoretical framework for organizing computations of networks of spiking neurons. In particular, we show that a suitable design enables them to solve hard constraint satisfaction problems from the domains of planning - optimization and verification - logical inference. The underlying design principles employ noise as a computational resource. Nevertheless the timing of spikes (rather than just spike rates) plays an essential role in the resulting computations. Furthermore, one can demonstrate for the Traveling Salesman Problem a surprising computational advantage of networks of spiking neurons compared with traditional artificial neural networks and Gibbs sampling. The identification of such advantage has been a well-known open problem.

Connected through hyperedge HE-1 (induced by source paper "integrating temporal information to spatial information in a neural circuit"):
  with members:
    - "counting to ten with two fingers compressed counting with spiking neurons"
    - "a basic compositional model for spiking neural networks"
    - "on the algorithmic power of spiking neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"

Connected through hyperedge HE-2 (induced by source paper "on the algorithmic power of spiking neural networks"):
  with members:
    - "a basic compositional model for spiking neural networks"
    - "dictionary learning by dynamical neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "an event based architecture for solving constraint satisfaction problems"
    - "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"

Connected through hyperedge HE-3 (induced by source paper "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"):
  with members:
    - "an event based architecture for solving constraint satisfaction problems"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "the thermodynamic temperature of a rhythmic spiking network"

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)