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 "improving low resource cross lingual document retrieval by reranking with deep bilingual representations"):
  Abstract: In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the MATERIAL dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.

Includes members:
  Member "deep relevance ranking using enhanced document query interactions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mrnn a multi resolution neural network with duplex attention for document retrieval in the context of question answering")
  Member "end to end neural ad hoc ranking with kernel pooling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "co pacrr a context aware neural ir model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable time budget constrained contextualization for re ranking")
  Member "pacrr a position aware neural ir model for relevance matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "deep architectures for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting low resource neural machine translation a case study")
  Member "word translation without parallel data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep graph matching consensus")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "a study of matchpyramid models on ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep look into neural ranking models for information retrieval")

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)