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: "on the exact relationship between the denoising function and the data distribution"
  Abstract: We prove an exact relationship between the optimal denoising function and the data distribution in the case of additive Gaussian noise, showing that denoising implicitly models the structure of data allowing it to be exploited in the unsupervised learning of representations. This result generalizes a known relationship [2], which is valid only in the limit of small corruption noise.

Connected through hyperedge HE-1 (induced by source paper "regularizing trajectory optimization with denoising autoencoders"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "improving model based control and active exploration with reconstruction uncertainty optimization"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"

Connected through hyperedge HE-2 (induced by source paper "recurrent ladder networks"):
  with members:
    - "semi supervised phoneme recognition with recurrent ladder networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "neural expectation maximization"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "learning recurrent representations for hierarchical behavior modeling"
    - "feynman machine the universal dynamical systems computer"

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)