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: "approximation with random bases"
  Abstract: In this work we discuss the problem of selecting suitable approximators from families of parameterized elementary functions that are known to be dense in a Hilbert space of functions. We consider and analyze published procedures, both randomized and deterministic, for selecting elements from these families that have been shown to ensure the rate of convergence in L2 norm of order O(1/N), where N is the number of elements. We show that both randomized and deterministic procedures are successful if additional information about the families of functions to be approximated is provided. In the absence of such additional information one may observe exponential growth of the number of terms needed to approximate the function and/or extreme sensitivity of the outcome of the approximation to parameters. Implications of our analysis for applications of neural networks in modeling and control are illustrated with examples.

Connected through hyperedge HE-1 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "what s hidden in a randomly weighted neural network"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "deep semi random features for nonlinear function approximation"
    - "stochastic configuration networks fundamentals and algorithms"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "deep stochastic configuration networks with universal approximation property"

Connected through hyperedge HE-2 (induced by source paper "local intrinsic dimensionality estimators based on concentration of measure"):
  with members:
    - "high dimensional brain in a high dimensional world blessing of dimensionality"
    - "intrinsic dimension estimation for locally undersampled data"
    - "lower bounds on adversarial robustness from optimal transport"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "blessing of dimensionality at the edge"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"

Connected through hyperedge HE-4 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "stochastic separation theorems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "knowledge transfer between artificial intelligence systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "blessing of dimensionality at the edge"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"

Connected through hyperedge HE-6 (induced by source paper "improving randomized learning of feedforward neural networks by appropriate generation of random parameters"):
  with members:
    - "stochastic configuration networks fundamentals and algorithms"

Connected through hyperedge HE-7 (induced by source paper "black box adversarial attacks with limited queries and information"):
  with members:
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "black box attacks against rnn based malware detection algorithms"
    - "on the limitation of convolutional neural networks in recognizing negative images"
    - "robust physical world attacks on machine learning models"
    - "simple black box adversarial perturbations for deep networks"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-8 (induced by source paper "correction of ai systems by linear discriminants probabilistic foundations"):
  with members:
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "how deep should be the depth of convolutional neural networks a backyard dog case study"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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.NA(Numerical Analysis)