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: "feynman machine the universal dynamical systems computer"
  Abstract: Efforts at understanding the computational processes in the brain have met with limited success, despite their importance and potential uses in building intelligent machines. We propose a simple new model which draws on recent findings in Neuroscience and the Applied Mathematics of interacting Dynamical Systems. The Feynman Machine is a Universal Computer for Dynamical Systems, analogous to the Turing Machine for symbolic computing, but with several important differences. We demonstrate that networks and hierarchies of simple interacting Dynamical Systems, each adaptively learning to forecast its evolution, are capable of automatically building sensorimotor models of the external and internal world. We identify such networks in mammalian neocortex, and show how existing theories of cortical computation combine with our model to explain the power and flexibility of mammalian intelligence. These findings lead directly to new architectures for machine intelligence. A suite of software implementations has been built based on these principles, and applied to a number of spatiotemporal learning tasks.

Connected through hyperedge HE-1 (induced by source paper "toyarchitecture unsupervised learning of interpretable models of the world"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a neurally inspired hierarchical prediction network for spatiotemporal sequence learning and prediction"
    - "unsupervised predictive memory in a goal directed agent"
    - "the consciousness prior"
    - "a simple neural network module for relational reasoning"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "dynamic routing between capsules"
    - "neural symbolic learning and reasoning a survey and interpretation"

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"
    - "on the exact relationship between the denoising function and the data distribution"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "learning recurrent representations for hierarchical behavior modeling"

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)