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: "correction of ai systems by linear discriminants probabilistic foundations"
  Abstract: Abstract   Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the systems can create new errors and is not always possible due to the resources involved. The important challenge is to develop fast methods to correct errors without damaging existing skills. We formulated the technical requirements to the ‘ideal’ correctors. Such correctors include binary classifiers, which separate the situations with high risk of errors from the situations where the AI systems work properly. Surprisingly, for essentially high-dimensional data such methods are possible: simple linear Fisher discriminant can separate the situations with errors from correctly solved tasks even for exponentially large samples. The paper presents the probabilistic basis for fast non-destructive correction of AI systems. A series of new stochastic separation theorems is proven. These theorems provide new instruments for fast non-iterative correction of errors of legacy AI systems. The new approaches become efficient in high-dimensions, for correction of high-dimensional systems in high-dimensional world (i.e. for processing of essentially high-dimensional data by large systems).  We prove that this separability property holds for a wide class of distributions including log-concave distributions and distributions with a special ‘SMeared Absolute Continuity’ (SmAC) property defined through relations between the volume and probability of sets of vanishing volume. These classes are much wider than the Gaussian distributions. The requirement of independence and identical distribution of data is significantly relaxed. The results are supported by computational analysis of empirical data sets.

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"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"

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"
    - "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"
    - "approximation with random bases"

Connected through hyperedge HE-3 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"):
  with members:
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "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-4 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain"):
  with members:
    - "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"
    - "approximation with random bases"
    - "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"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "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-6 (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"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "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-7 (induced by source paper "ecg segmentation by neural networks errors and correction"):
  with members:
    - "visual interpretability for deep learning a survey"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "understanding neural networks through deep visualization"

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)