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: "rand walk a latent variable model approach to word embeddings"
  Abstract: Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. #R##N#This paper proposes a new generative model, a dynamic version of the log-linear topic model of~\citet{mnih2007three}. The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by~\citet{mikolov2013efficient} and many subsequent papers. #R##N#Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.

Connected through hyperedge HE-1 (induced by source paper "understanding composition of word embeddings via tensor decomposition"):
  with members:
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "advances in pre training distributed word representations"
    - "tensorflow a system for large scale machine learning"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "mathematical foundations for a compositional distributional model of meaning"
    - "most tensor problems are np hard"

Connected through hyperedge HE-2 (induced by source paper "dynamic word embeddings for evolving semantic discovery"):
  with members:
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "a latent variable model approach to pmi based word embeddings"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "temporal analysis of language through neural language models"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "cover learning covariate specific vector representations with tensor decompositions"):
  with members:
    - "adam a method for stochastic optimization"
    - "temporal analysis of language through neural language models"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-4 (induced by source paper "recovery guarantee of weighted low rank approximation via alternating minimization"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "tighter low rank approximation via sampling the leveraged element"
    - "understanding alternating minimization for matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-5 (induced by source paper "linear algebraic structure of word senses with applications to polysemy"):
  with members:
    - "the mechanism of additive composition"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "sparse overcomplete word vector representations"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

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)