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 "working hard or hardly working challenges of integrating typology into neural dependency parsers"):
  Abstract: This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.

Includes members:
  Member "low resource syntactic transfer with unsupervised source reordering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross lingual dependency parsing with unlabeled auxiliary languages")
  Member "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "on difficulties of cross lingual transfer with order differences a case study on dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "the galactic dependencies treebanks getting more data by synthesizing new languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving cross lingual model transfer by chunking")
  Member "fine grained prediction of syntactic typology discovering latent structure with supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "studying the inductive biases of rnns with synthetic variations of natural languages")
  Member "survey on the use of typological information in natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "deep biaffine attention for neural dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "google s multilingual neural machine translation system enabling zero shot translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)