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: "input anticipating critical reservoirs show power law forgetting of unexpected input events"
  Abstract: Usually, reservoir computing shows an exponential memory decay. This paper investigates under which circumstances echo state networks can show a power law forgetting. That means traces of earlier events can be found in the reservoir for very long time spans. Such a setting requires critical connectivity exactly at the limit of what is permissible according the echo state condition. However, for general matrices the limit cannot be determined exactly from theory. In addition, the behavior of the network is strongly influenced by the input flow. Results are presented that use certain types of restricted recurrent connectivity and anticipation learning with regard to the input, where indeed power law forgetting can be achieved.

Connected through hyperedge HE-1 (induced by source paper "multiplex visibility graphs to investigate recurrent neural network dynamics"):
  with members:
    - "architectural complexity measures of recurrent neural networks"
    - "distributed sequence memory of multidimensional inputs in recurrent networks"
    - "deep learning in neural networks"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-2 (induced by source paper "orthogonal echo state networks and stochastic evaluations of likelihoods"):
  with members:
    - "training echo state networks with regularization through dimensionality reduction"
    - "echo state condition at the critical point"

Connected through hyperedge HE-3 (induced by source paper "echo state condition at the critical point"):
  with members:
    - "orthogonal echo state networks and stochastic evaluations of likelihoods"
    - "critical echo state networks that anticipate input using morphable transfer functions"

Connected through hyperedge HE-4 (induced by source paper "critical echo state networks that anticipate input using morphable transfer functions"):
  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)