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 "stem driven language models for morphologically rich languages"):
  Abstract: Neural language models (LMs) have shown to benefit significantly from enhancing word vectors with subword-level information, especially for morphologically rich languages. This has been mainly tackled by providing subword-level information as an input; using subword units in the output layer has been far less explored. In this work, we propose LMs that are cognizant of the underlying stems in each word. We derive stems for words using a simple unsupervised technique for stem identification. We experiment with different architectures involving multi-task learning and mixture models over words and stems. We focus on four morphologically complex languages -- Hindi, Tamil, Kannada and Finnish -- and observe significant perplexity gains with using our stem-driven LMs when compared with other competitive baseline models.

Includes members:
  Member "morphological segmentation inside out":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural factor graph models for cross lingual morphological tagging")
  Member "compositional representation of morphologically rich input for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")
  Member "from characters to words to in between do we capture morphology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "character word lstm language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adapting multilingual neural machine translation to unseen languages")
  Member "breaking the softmax bottleneck a high rank rnn language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "a joint model for word embedding and word morphology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "authorship attribution in bangla literature using character level cnn")
  Member "word representation models for morphologically rich languages in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "generalizing and hybridizing count based and neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent relation language models")

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)