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: "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
  Abstract: Human translators routinely have to translate rare inflections of words - due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as hablaramos. Note the lexeme itself, hablar, is relatively common. In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization. We introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the state-of-the-art models on the task of translation of less frequent morphological forms. We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology.

Connected through hyperedge HE-1 (induced by source paper "should all cross lingual embeddings speak english"):
  with members:
    - "density matching for bilingual word embedding"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "lost in evaluation misleading benchmarks for bilingual dictionary induction"
    - "unsupervised statistical machine translation"
    - "unsupervised multilingual word embeddings"
    - "learning word vectors for 157 languages"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  with members:
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "an iterative closest point method for unsupervised word translation"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "unimorph 2 0 universal morphology"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"

Connected through hyperedge HE-3 (induced by source paper "exploiting cross lingual subword similarities in low resource document classification"):
  with members:
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "interactive refinement of cross lingual word embeddings"
    - "gromov wasserstein alignment of word embedding spaces"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

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)