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: "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
  Abstract: We initiate a line of investigation into biological neural networks from an algorithmic perspective. We develop a simplified but biologically plausible model for distributed computation in stochastic spiking neural networks and study tradeoffs between computation time and network complexity in this model. Our aim is to abstract real neural networks in a way that, while not capturing all interesting features, preserves high-level behavior and allows us to make biologically relevant conclusions. #R##N#In this paper, we focus on the important `winner-take-all' (WTA) problem, which is analogous to a neural leader election unit: a network consisting of $n$ input neurons and $n$ corresponding output neurons must converge to a state in which a single output corresponding to a firing input (the `winner') fires, while all other outputs remain silent. Neural circuits for WTA rely on inhibitory neurons, which suppress the activity of competing outputs and drive the network towards a converged state with a single firing winner. We attempt to understand how the number of inhibitors used affects network convergence time. #R##N#We show that it is possible to significantly outperform naive WTA constructions through a more refined use of inhibition, solving the problem in $O(\theta)$ rounds in expectation with just $O(\log^{1/\theta} n)$ inhibitors for any $\theta$. An alternative construction gives convergence in $O(\log^{1/\theta} n)$ rounds with $O(\theta)$ inhibitors. We compliment these upper bounds with our main technical contribution, a nearly matching lower bound for networks using $\ge \log\log n$ inhibitors. Our lower bound uses familiar indistinguishability and locality arguments from distributed computing theory. It lets us derive a number of interesting conclusions about the structure of any network solving WTA with good probability, and the use of randomness and inhibition within such a network.

Connected through hyperedge HE-1 (induced by source paper "winner take all computation in spiking neural networks"):
  with members:
    - "neuro ram unit with applications to similarity testing and compression in spiking neural 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"
    - "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 theoretical basis for efficient computations with noisy spiking neurons"

Connected through hyperedge HE-3 (induced by source paper "learning hierarchically structured concepts"):
  with members:
    - "a basic compositional model for spiking neural networks"
    - "interpreting deep visual representations via network dissection"
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"

Connected through hyperedge HE-4 (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"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"

Connected through hyperedge HE-5 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience"):
  with members:
    - "improved encoding and decoding for non adaptive threshold group testing"
    - "sublinear decoding schemes for non adaptive group testing with inhibitors"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "non adaptive group testing with inhibitors"
    - "near optimal stochastic threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "graph constrained group testing"
    - "noise resilient group testing limitations and constructions"

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)