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 "blessing of dimensionality at the edge"):
  Abstract: In this paper we present theory and algorithms enabling classes of Artificial Intelligence (AI) systems to continuously and incrementally improve with a-priori quantifiable guarantees - or more specifically remove classification errors - over time. This is distinct from state-of-the-art machine learning, AI, and software approaches. Another feature of this approach is that, in the supervised setting, the computational complexity of training is linear in the number of training samples. At the time of classification, the computational complexity is bounded by few inner product calculations. Moreover, the implementation is shown to be very scalable. This makes it viable for deployment in applications where computational power and memory are limited, such as embedded environments. It enables the possibility for fast on-line optimisation using improved training samples. The approach is based on the concentration of measure effects and stochastic separation theorems.

Includes members:
  Member "the unreasonable effectiveness of small neural ensembles in high dimensional brain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality")
  Member "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality")
  Member "estimating the effective dimension of large biological datasets using fisher separability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local intrinsic dimensionality estimators based on concentration of measure")
  Member "correction of ai systems by linear discriminants probabilistic foundations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local intrinsic dimensionality estimators based on concentration of measure")
  Member "augmented artificial intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "local intrinsic dimensionality estimators based on concentration of measure")
  Member "stochastic separation theorems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "deep randomized 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)