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 "tracking typological traits of uralic languages in distributed language representations"):
  Abstract: Although linguistic typology has a long history, computational approaches have only recently gained popularity. The use of distributed representations in computational linguistics has also become increasingly popular. A recent development is to learn distributed representations of language, such that typologically similar languages are spatially close to one another. Although empirical successes have been shown for such language representations, they have not been subjected to much typological probing. In this paper, we first look at whether this type of language representations are empirically useful for model transfer between Uralic languages in deep neural networks. We then investigate which typological features are encoded in these representations by attempting to predict features in the World Atlas of Language Structures, at various stages of fine-tuning of the representations. We focus on Uralic languages, and find that some typological traits can be automatically inferred with accuracies well above a strong baseline.

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 "learning language representations for typology prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "uncovering probabilistic implications in typological knowledge bases")
  Member "massively multilingual neural grapheme to phoneme conversion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse sequence to sequence models")
  Member "continuous multilinguality with language vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "polyglot neural language models a case study in cross lingual phonetic representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "google s multilingual neural machine translation system enabling zero shot translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention over parameters for dialogue systems")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)