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 "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"):
  Abstract: A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS). Doing this manually is prohibitively time-consuming, which is in part evidenced by the fact that only 100 out of over 7,000 languages spoken in the world are fully covered in WALS. #R##N#We learn distributed language representations, which can be used to predict typological properties on a massively multilingual scale. Additionally, quantitative and qualitative analyses of these language embeddings can tell us how language similarities are encoded in NLP models for tasks at different typological levels. The representations are learned in an unsupervised manner alongside tasks at three typological levels: phonology (grapheme-to-phoneme prediction, and phoneme reconstruction), morphology (morphological inflection), and syntax (part-of-speech tagging). #R##N#We consider more than 800 languages and find significant differences in the language representations encoded, depending on the target task. For instance, although Norwegian Bokm{\aa}l and Danish are typologically close to one another, they are phonologically distant, which is reflected in their language embeddings growing relatively distant in a phonological task. We are also able to predict typological features in WALS with high accuracies, even for unseen language families.

Includes members:
  Member "past present future a computational investigation of the typology of tense in 1000 languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "su rug at the conll sigmorphon 2017 shared task morphological inflection with attentional sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks")
  Member "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pushing the limits of low resource morphological inflection")
  Member "learning language representations for typology prediction":
    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 "probabilistic typology deep generative models of vowel inventories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "encoding of phonology in a recurrent neural model of grounded speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "massively multilingual neural grapheme to phoneme conversion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse sequence to sequence models")
  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)