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: "guided self organization of input driven recurrent neural networks"
  Abstract: To understand the world around us, our brains solve a variety of tasks. One of the crucial functions of a brain is to make predictions of what will happen next, or in the near future. This ability helps us to anticipate upcoming events and plan our reactions to them in advance. To make these predictions, past information needs to be stored, transformed or used otherwise. How exactly the brain achieves this information processing is far from clear and under heavy investigation. To guide this extraordinary research effort, neuroscientists increasingly look for theoretical frameworks that could help explain the data recorded from the brain, and to make the enormous task more manageable. This is evident, for instance, through the funding of the billion-dollar ”Human Brain Project”, of the European Union, amongst others. Mathematical techniques from graph and information theory, control theory, dynamical and complex systems (Sporns 2011), statistical mechanics (Rolls and Deco 2010), as well as machine learning and computer vision (Seung 2012; Hawkins and Blakeslee 2004), have provided new insights into brain structure and possible function, and continue to generate new hypotheses for future research.

Connected through hyperedge HE-1 (induced by source paper "dynamical systems as temporal feature spaces"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "gated orthogonal recurrent units on learning to forget"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"

Connected through hyperedge HE-2 (induced by source paper "input anticipating critical reservoirs show power law forgetting of unexpected input events"):
  with members:
    - "echo state condition at the critical point"

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)