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: "word2vec skip gram with negative sampling is a weighted logistic pca"
  Abstract: We show that the skip-gram formulation of word2vec trained with negative sampling is equivalent to a weighted logistic PCA. This connection allows us to better understand the objective, compare it to other word embedding methods, and extend it to higher dimensional models.

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"
    - "multiview kernels for low dimensional modeling of seismic events"
    - "kernel scaling for manifold learning and classification"
    - "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 "what the vec towards probabilistically grounded embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "on understanding knowledge graph representation"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "quantum inspired complex word embedding"
    - "towards understanding linear word analogies"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "node2vec scalable feature learning for networks"
    - "issues in evaluating semantic spaces using word analogies"

Connected through hyperedge HE-3 (induced by source paper "revisiting skip gram negative sampling model with regularization"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding composition of word embeddings via tensor decomposition"
    - "riemannian optimization for skip gram negative sampling"
    - "advances in pre training distributed word representations"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "greedy approaches to symmetric orthogonal tensor decomposition"
    - "neural architectures for named entity recognition"
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"

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.CL(Computation and Language)