human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "augmented artificial intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain")
  Member "blessing of dimensionality mathematical foundations of the statistical physics of data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local intrinsic dimensionality estimators based on concentration of measure")
  Member "augmented artificial intelligence a conceptual framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain")
  Member "how deep should be the depth of convolutional neural networks a backyard dog case study":
    also connected through other hyperedges:
      (none)
  Member "stochastic separation theorems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality")
  Member "stochastic configuration networks fundamentals and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep randomized neural networks")
  Member "neurogenesis deep learning extending deep networks to accommodate new classes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")

Question: Please tell me which class the source paper 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)