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: "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
  Abstract: We study distributed algorithms implemented in a simplified biologically inspired model for stochastic spiking neural networks. We focus on tradeoffs between computation time and network complexity, along with the role of randomness in efficient neural computation. #R##N#It is widely accepted that neural computation is inherently stochastic. In recent work, we explored how this stochasticity could be leveraged to solve the `winner-take-all' leader election task. Here, we focus on using randomness in neural algorithms for similarity testing and compression. In the most basic setting, given two $n$-length patterns of firing neurons, we wish to distinguish if the patterns are equal or $\epsilon$-far from equal. #R##N#Randomization allows us to solve this task with a very compact network, using $O \left (\frac{\sqrt{n}\log n}{\epsilon}\right)$ auxiliary neurons, which is sublinear in the input size. At the heart of our solution is the design of a $t$-round neural random access memory, or indexing network, which we call a neuro-RAM. This module can be implemented with $O(n/t)$ auxiliary neurons and is useful in many applications beyond similarity testing. #R##N#Using a VC dimension-based argument, we show that the tradeoff between runtime and network size in our neuro-RAM is nearly optimal. Our result has several implications -- since our neuro-RAM can be implemented with deterministic threshold gates, it shows that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem. It also establishes a separation between feedforward networks whose gates spike with sigmoidal probability functions, and well-studied deterministic sigmoidal networks, whose gates output real number sigmoidal values, and which can implement a neuro-RAM much more efficiently.

Connected through hyperedge HE-1 (induced by source paper "winner take all computation in spiking neural networks"):
  with members:
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "simple efficient and neural algorithms for sparse coding"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "uniform sampling for matrix approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "random projections for k means clustering"

Connected through hyperedge HE-2 (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"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "a theoretical basis for efficient computations with noisy spiking neurons"

Connected through hyperedge HE-3 (induced by source paper "counting to ten with two fingers compressed counting with spiking neurons"):
  with members:
    - "integrating temporal information to spatial information in a neural circuit"
    - "a basic compositional model for spiking neural networks"
    - "on the algorithmic power of spiking neural networks"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning precise spike train to spike train transformations in multilayer feedforward neuronal networks"

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)