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 "multi task deep morphological analyzer context aware joint morphological tagging and lemma prediction"):
  Abstract: Morphological analysis is an important first step in downstream tasks like machine translation and dependency parsing of morphologically rich languages (MRLs) such as those belonging to Indo-Aryan and Dravidian families. However, the ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits a notoriously complicated task for MRLs. We propose a character-level neural morphological analyzer, the Multi Task Deep Morphological analyzer (MT-DMA), based on multitask learning of word-level tag markers for Hindi. In order to show the portability of our system to other related languages, we present results on Urdu too. MT-DMA predicts the complete set of morphological tags for words of Indo-Aryan languages: Parts-of-speech (POS), Gender (G), Number (N), Person (P), Case (C), Tense-Aspect-Modality (TAM) marker as well as the Lemma (L) by jointly learning all these in a single end-to-end framework. We show the effectiveness of training of such deep neural networks by the simultaneous optimization of multiple loss functions and sharing of initial parameters for context-aware morphological analysis. Our model outperforms the state-of-art analyzers for Hindi and Urdu. Exploring the use of a set of character-level features in phonological space optimized for each tag through a multi-objective genetic algorithm, coupled with effective training strategies, our model establishes a new state-of-the-art accuracy score upon all seven of the tasks for both the languages. MT-DMA is publicly accessible to be used at this http URL

Includes members:
  Member "neural machine translation for bilingually scarce scenarios a deep multi task learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "meta multi task learning for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to few shot learn across diverse natural language classification tasks")
  Member "scheduled multi task learning from syntax to translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "online and linear time attention by enforcing monotonic alignments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition")
  Member "cross lingual character level neural morphological tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "char rnn for word stress detection in east slavic languages")
  Member "improving generalization performance by switching from adam to sgd":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "recent trends in deep learning based natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "exploiting linguistic resources for neural machine translation using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to multi task learn for better neural machine translation")

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)