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: "an event based architecture for solving constraint satisfaction problems"
  Abstract: Constraint satisfaction problems are ubiquitous in many domains. They are typically solved using conventional digital computing architectures that do not reflect the distributed nature of many of these problems, and are thus ill-suited for solving them. Here we present a parallel analogue/digital hardware architecture specifically designed to solve such problems. We cast constraint satisfaction problems as networks of stereotyped nodes that communicate using digital pulses, or events. Each node contains an oscillator implemented using analogue circuits. The non-repeating phase relations among the oscillators drive the exploration of the solution space. We show that this hardware architecture can yield state-of-the-art performance on random SAT problems under reasonable assumptions on the implementation. We present measurements from a prototype electronic chip to demonstrate that a physical implementation of the proposed architecture is robust to practical non-idealities and to validate the theory proposed.

Connected through hyperedge HE-1 (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"
    - "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"
    - "learning precise spike train to spike train transformations in multilayer feedforward neuronal networks"

Connected through hyperedge HE-2 (induced by source paper "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"):
  with members:
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "optimization hardness as transient chaos in an analog approach to constraint satisfaction"
    - "a new look at the easy hard easy pattern of combinatorial search difficulty"

Connected through hyperedge HE-3 (induced by source paper "computing with dynamical systems based on insulator metal transition oscillators"):
  with members:
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"

Connected through hyperedge HE-4 (induced by source paper "efficient analog circuits for boolean satisfiability"):
  with members:
    - "optimization hardness as transient chaos in an analog approach to constraint satisfaction"

Connected through hyperedge HE-5 (induced by source paper "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"):
  with members:
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "a theoretical basis for efficient computations with noisy spiking neurons"
    - "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.DC(Distributed, Parallel, and Cluster Computing)