human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a probabilistic generative model of linguistic typology"):
  Abstract: In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features. The implied covariance between features inspires our probabilisation of this line of linguistic inquiry---we develop a generative model of language based on exponential-family matrix factorisation. By modelling all languages and features within the same architecture, we show how structural similarities between languages can be exploited to predict typological features with near-perfect accuracy, outperforming several baselines on the task of predicting held-out features. Furthermore, we show that language embeddings pre-trained on monolingual text allow for generalisation to unobserved languages. This finding has clear practical and also theoretical implications: the results confirm what linguists have hypothesised, i.e.~that there are significant correlations between typological features and languages.

Includes members:
  Member "what do language representations really represent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "semantic drift in multilingual representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "modeling language variation and universals a survey on typological linguistics for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "past present future a computational investigation of the typology of tense in 1000 languages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "learning language representations for typology prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "probabilistic typology deep generative models of vowel inventories":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "tracking typological traits of uralic languages in distributed language representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")

Question: Please tell me which class the source paper 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)