human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "cross linguistic differences and similarities in image descriptions"
  Abstract: Automatic image description systems are commonly trained and evaluated on large image description datasets. Recently, researchers have started to collect such datasets for languages other than English. An unexplored question is how different these datasets are from English and, if there are any differences, what causes them to differ. This paper provides a cross-linguistic comparison of Dutch, English, and German image descriptions. We find that these descriptions are similar in many respects, but the familiarity of crowd workers with the subjects of the images has a noticeable influence on description specificity.

Connected through hyperedge HE-1 (induced by source paper "zero resource neural machine translation with multi agent communication game"):
  with members:
    - "a teacher student framework for zero resource neural machine translation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "attention is all you need"
    - "image pivoting for learning multilingual multimodal representations"
    - "convolutional sequence to sequence learning"
    - "zero resource translation with multi lingual neural machine translation"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"

Connected through hyperedge HE-2 (induced by source paper "findings of the second shared task on multimodal machine translation and multilingual image description"):
  with members:
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "osu multimodal machine translation system report"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "edinburgh neural machine translation systems for wmt 16"

Question: Please tell me which class the center node 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)