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: "blessing of dimensionality mathematical foundations of the statistical physics of data"
  Abstract: The concentrations of measure phenomena were discovered as the mathematical background to statistical mechanics at the end of the nineteenth/beginning of the twentieth century and have been explored in mathematics ever since. At the beginning of the twenty-first century, it became clear that the proper utilization of these phenomena in machine learning might transform the  curse of dimensionality  into the  blessing of dimensionality . This paper summarizes recently discovered phenomena of measure concentration which drastically simplify some machine learning problems in high dimension, and allow us to correct legacy artificial intelligence systems. The classical concentration of measure theorems state that i.i.d. random points are concentrated in a thin layer near a surface (a sphere or equators of a sphere, an average or median-level set of energy or another Lipschitz function, etc.). The new  stochastic separation theorems  describe the thin structure of these thin layers: the random points are not only concentrated in a thin layer but are all linearly separable from the rest of the set, even for exponentially large random sets. The linear functionals for separation of points can be selected in the form of the linear Fisher’s discriminant. All artificial intelligence systems make errors. Non-destructive correction requires separation of the situations (samples) with errors from the samples corresponding to correct behaviour by a simple and robust classifier. The stochastic separation theorems provide us with such classifiers and determine a non-iterative (one-shot) procedure for their construction.  This article is part of the theme issue ‘Hilbert’s sixth problem’.

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

Connected through hyperedge HE-3 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks"):
  with members:
    - "orthogonal deep neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning both weights and connections for efficient neural networks"
    - "shapenet an information rich 3d model repository"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-4 (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"
    - "stochastic separation theorems"
    - "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-5 (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"
    - "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"
    - "approximation with random bases"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "stochastic separation theorems"
    - "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-7 (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"
    - "stochastic separation theorems"
    - "intrinsic dimension estimation of data by principal component analysis"
    - "principal graphs and manifolds"
    - "topological grammars for data approximation"

Connected through hyperedge HE-8 (induced by source paper "correction of ai systems by linear discriminants probabilistic foundations"):
  with members:
    - "augmented artificial intelligence"
    - "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"
    - "knowledge transfer between artificial intelligence systems"

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)