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: "tracking the evolution of words with time reflective text representations"
  Abstract: More than 80% of today's data is unstructured in nature, and these unstructured datasets evolving over time. A large part of the evolving unstructured data are text documents generated by media outlets, scholarly articles in digital libraries, and social media. Vector space models have been developed to analyze text documents using data mining and machine learning algorithms. While ample vector space models exist for text data, the evolution aspect of evolving text corpora is still missing in vector-based representations. The advent of word embeddings has given a way to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully yet. The inclusion of the time aspect in the feature space will provide vectors for every natural language element, such as words or entities, at every timestamp. Such temporal word vectors will provide the ability to track how the meaning of a word changes over time, in terms of the changes in its neighborhood. Moreover, a time-reflective text representation will pave the way to a new set of text analytic abilities involving time series for text collections. #R##N#In this paper, we present the potential benefits of a time-reflective vector space model for temporal text data that is able to capture short and long-term changes in the meaning of words. We compare our approach with the limited literature on dynamic embeddings. We present qualitative and quantitative evaluations using semantic evolution tracking as the target application.

Connected through hyperedge HE-1 (induced by source paper "training temporal word embeddings with a compass"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "learning distributed representations of texts and entities from knowledge base"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "semantics derived automatically from language corpora contain human like biases"
    - "tracing metaphors in time through self distance in vector spaces"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "document classification by inversion of distributed language representations"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-2 (induced by source paper "an improved historical embedding without alignment"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "dynamic word embeddings for evolving semantic discovery"
    - "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"
    - "statistically significant detection of linguistic change"

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)