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: "multiview kernels for low dimensional modeling of seismic events"
  Abstract: The problem of learning from seismic recordings has been studied for years. There is a growing interest of developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability to have a reliable identification of man-made explosions. The availability of multiple high-dimensional observations has increased the use of machine learning techniques in a variety of fields. In this paper, we propose to use a kernel-fusion-based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and Jordan. The method achieves promising results in the classification of event type as well as the estimation of the event location. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.

Connected through hyperedge HE-1 (induced by source paper "the spectral underpinning of word2vec"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "kernel scaling for manifold learning and classification"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "geometry based data generation"):
  with members:
    - "kernel scaling for manifold learning and classification"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-3 (induced by source paper "kernel scaling for manifold learning and classification"):
  with members:
    - "multi view kernel consensus for data analysis"
    - "multi view kernel consensus for data analysis and signal processing"
    - "multiview diffusion maps"
    - "danco dimensionality from angle and norm concentration"

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)