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: "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
  Abstract: We introduce a new measure of distance between languages based on word embedding, called word embedding language divergence (WELD). WELD is defined as divergence between unified similarity distribution of words between languages. Using such a measure, we perform language comparison for fifty natural languages and twelve genetic languages. Our natural language dataset is a collection of sentence-aligned parallel corpora from bible translations for fifty languages spanning a variety of language families. Although we use parallel corpora, which guarantees having the same content in all languages, interestingly in many cases languages within the same family cluster together. In addition to natural languages, we perform language comparison for the coding regions in the genomes of 12 different organisms (4 plants, 6 animals, and two human subjects). Our result confirms a significant high-level difference in the genetic language model of humans/animals versus plants. The proposed method is a step toward defining a quantitative measure of similarity between languages, with applications in languages classification, genre identification, dialect identification, and evaluation of translations.

Connected through hyperedge HE-1 (induced by source paper "semantic drift in multilingual representations"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "filtering and mining parallel data in a joint multilingual space"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "multimodal grounding for language processing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "unisent universal adaptable sentiment lexica for 1000 languages"):
  with members:
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "word translation without parallel data"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-3 (induced by source paper "past present future a computational investigation of the typology of tense in 1000 languages"):
  with members:
    - "learning language representations for typology prediction"
    - "faster decoding for subword level phrase based smt between related languages"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "cross lingual annotation projection of semantic roles"

Connected through hyperedge HE-4 (induced by source paper "mmcr4nlp multilingual multiway corpora repository for natural language processing"):
  with members:
    - "multi source neural translation"

Connected through hyperedge HE-5 (induced by source paper "nonsymbolic text representation"):
  with members:
    - "character based neural machine translation"
    - "online segment to segment neural transduction"
    - "a joint model for word embedding and word morphology"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "gated word character recurrent language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-6 (induced by source paper "language classification from bilingual word embedding graphs"):
  with members:
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "freshman or fresher quantifying the geographic variation of internet language"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"

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)