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: "geometry and expressive power of conditional restricted boltzmann machines"
  Abstract: Conditional restricted Boltzmann machines are undirected stochastic neural networks with a layer of input and output units connected bipartitely to a layer of hidden units. These networks define models of conditional probability distributions on the states of the output units given the states of the input units, parametrized by interaction weights and biases. We address the representational power of these models, proving results their ability to represent conditional Markov random fields and conditional distributions with restricted supports, the minimal size of universal approximators, the maximal model approximation errors, and on the dimension of the set of representable conditional distributions. We contribute new tools for investigating conditional probability models, which allow us to improve the results that can be derived from existing work on restricted Boltzmann machine probability models.

Connected through hyperedge HE-1 (induced by source paper "stochastic feedforward neural networks universal approximation"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "simplified stochastic feedforward neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep skinny neural networks are not universal approximators"):
  with members:
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "expressive power of recurrent neural networks"
    - "the loss surface of deep and wide neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-3 (induced by source paper "restricted boltzmann machines introduction and review"):
  with members:
    - "revisiting natural gradient for deep networks"
    - "finding the maximizers of the information divergence from an exponential family"
    - "information theoretic inference of common ancestors"

Connected through hyperedge HE-4 (induced by source paper "a theory of cheap control in embodied systems"):
  with members:
    - "quantifying morphological computation based on an information decomposition of the sensorimotor loop"
    - "quantifying morphological computation"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"
    - "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"

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)