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: "efficient design of hardware enabled reservoir computing in fpgas"
  Abstract: In this work, we propose a new approach toward the efficient optimization and implementation of reservoir computing hardware, reducing the required domain-expert knowledge and optimization effort. First, we introduce a self-adapting reservoir input mask to the structure of the data via linear autoencoders. We, therefore, incorporate the advantages of dimensionality reduction and dimensionality expansion achieved by conventional algorithmically-efficient linear algebra procedures of principal component analysis. Second, we employ evolutionary-inspired genetic algorithm techniques resulting in a highly efficient optimization of reservoir dynamics with a dramatically reduced number of evaluations comparing to exhaustive search. We illustrate the method on the so-called single-node reservoir computing architecture, especially suitable for implementation in ultrahigh-speed hardware. The combination of both methods and the resulting reduction of time required for performance optimization of a hardware system establish a strategy toward machine learning hardware capable of self-adaption to optimally solve specific problems. We confirm the validity of those principles building reservoir computing hardware based on a field-programmable gate array.In this work, we propose a new approach toward the efficient optimization and implementation of reservoir computing hardware, reducing the required domain-expert knowledge and optimization effort. First, we introduce a self-adapting reservoir input mask to the structure of the data via linear autoencoders. We, therefore, incorporate the advantages of dimensionality reduction and dimensionality expansion achieved by conventional algorithmically-efficient linear algebra procedures of principal component analysis. Second, we employ evolutionary-inspired genetic algorithm techniques resulting in a highly efficient optimization of reservoir dynamics with a dramatically reduced number of evaluations comparing to exhaustive search. We illustrate the method on the so-called single-node reservoir computing architecture, especially suitable for implementation in ultrahigh-speed hardware. The combination of both methods and the resulting reduction of time required for performance optimization of a hardware system es...

Connected through hyperedge HE-1 (induced by source paper "combining machine learning with knowledge based modeling for scalable forecasting and subgrid scale closure of large complex spatiotemporal systems"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

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.ET(Emerging Technologies)