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: "stochastic separation theorems"
  Abstract: Abstract   The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples where the system works properly. We demonstrate that in (moderately) high dimension this separation could be achieved with probability close to one by linear discriminants. Based on fundamental properties of measure concentration, we show that for   M    a  exp   (  b  n  )    random   M  -element sets in     R    n     are linearly separable with probability   p  ,   p  >  1  −  ϑ  , where   1  >  ϑ  >  0   is a given small constant. Exact values of   a  ,  b  >  0   depend on the probability distribution that determines how the random   M  -element sets are drawn, and on the constant   ϑ  . These  stochastic separation theorems  provide a new instrument for the development, analysis, and assessment of machine learning methods and algorithms in high dimension. Theoretical statements are illustrated with numerical examples.

Connected through hyperedge HE-1 (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"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "approximation with random bases"

Connected through hyperedge HE-2 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic configuration networks fundamentals and algorithms"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "deep stochastic configuration networks with universal approximation property"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "using deep convolutional networks for gesture recognition in american sign language"

Connected through hyperedge HE-3 (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"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "knowledge transfer between artificial intelligence systems"
    - "approximation with random bases"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (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 configuration networks fundamentals and algorithms"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

Connected through hyperedge HE-5 (induced by source paper "estimating the effective dimension of large biological datasets using fisher separability analysis"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "robust and scalable learning of data manifolds with complex topologies via elpigraph"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "intrinsic dimension estimation of data by principal component analysis"
    - "principal graphs and manifolds"
    - "topological grammars for data approximation"

Connected through hyperedge HE-6 (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 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"
    - "knowledge transfer between artificial intelligence systems"

Connected through hyperedge HE-7 (induced by source paper "blessing of dimensionality mathematical foundations of the statistical physics of data"):
  with members:
    - "stochastic configuration networks fundamentals and algorithms"
    - "knowledge transfer between artificial intelligence systems"
    - "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"
    - "approximation with random bases"
    - "caffe convolutional architecture for fast feature embedding"
    - "data complexity measured by principal graphs"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"

Connected through hyperedge HE-8 (induced by source paper "robust and scalable learning of complex dataset topologies via elpigraph"):
  with members:
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "robust principal graphs for data approximation"
    - "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"
    - "data complexity measured by principal graphs"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "topological grammars for data approximation"

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.LG(Machine Learning)