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 "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  Abstract: Logographs (Chinese characters) have recursive structures (i.e. hierarchies of sub-units in logographs) that contain phonological and semantic information, as developmental psychology literature suggests that native speakers leverage on the structures to learn how to read. Exploiting these structures could potentially lead to better embeddings that can benefit many downstream tasks. We propose building hierarchical logograph (character) embeddings from logograph recursive structures using treeLSTM, a recursive neural network. Using recursive neural network imposes a prior on the mapping from logographs to embeddings since the network must read in the sub-units in logographs according to the order specified by the recursive structures. Based on human behavior in language learning and reading, we hypothesize that modeling logographs’ structures using recursive neural network should be beneficial. To verify this claim, we consider two tasks (1) predicting logographs’ Cantonese pronunciation from logographic structures and (2) language modeling. Empirical results show that the proposed hierarchical embeddings outperform baseline approaches. Diagnostic analysis suggests that hierarchical embeddings constructed using treeLSTM is less sensitive to distractors, thus is more robust, especially on complex logographs.

Includes members:
  Member "attentive mimicking better word embeddings by attending to informative contexts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes")
  Member "phonology augmented statistical framework for machine transliteration using limited linguistic resources":
    also connected through other hyperedges:
      (none)
  Member "chinese japanese unsupervised neural machine translation using sub character level information":
    also connected through other hyperedges:
      (none)
  Member "neural machine translation of logographic languages using sub character level information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how transformer revitalizes character based neural machine translation an investigation on japanese vietnamese translation systems")
  Member "generalizing word embeddings using bag of subwords":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network")
  Member "dual long short term memory networks for sub character representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal neural pronunciation modeling for spoken languages with logographic origin")
  Member "extrapolation in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "location attention for extrapolation to longer sequences")
  Member "deep contextualized word representations":
    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")

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)