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 "open source dataset and machine learning techniques for automatic recognition of historical graffiti"):
  Abstract: Machine learning techniques are presented for automatic recognition of the historical letters (XI–XVIII centuries) carved on the stoned walls of St. Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.

Includes members:
  Member "capsule deep neural network for recognition of historical graffiti handwriting":
    also connected through other hyperedges:
      (none)
  Member "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")
  Member "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")
  Member "offline handwritten signature verification literature review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")

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)