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: "learning word relatedness over time"
  Abstract: Search systems are often focused on providing relevant results for the "now", assuming both corpora and user needs that focus on the present. However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books. Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge. Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally. In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections. The model supports the task of identifying, given two words, when they relate to each other. We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.

Connected through hyperedge HE-1 (induced by source paper "soulmate short text author linking through multi aspect temporal textual embedding"):
  with members:
    - "teals time aware text embedding approach to leverage subgraphs"
    - "improving topic models with latent feature word representations"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "node2vec scalable feature learning for networks"
    - "e commerce in your inbox product recommendations at scale"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "teals time aware text embedding approach to leverage subgraphs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "diachronic word embeddings and semantic shifts a survey"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "incremental skip gram model with negative sampling"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "statistically significant detection of linguistic change"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-4 (induced by source paper "tracking the evolution of words with time reflective text representations"):
  with members:
    - "a state of the art of semantic change computation"
    - "dynamic word embeddings for evolving semantic discovery"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "bayesian neural word embedding"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "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"

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)