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: "bootstrapping transliteration with constrained discovery for low resource languages"
  Abstract: Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction. Existing approaches to transliteration generation require a large (>5000) number of training examples. This difficulty contrasts with transliteration discovery, a somewhat easier task that involves picking a plausible transliteration from a given list. In this work, we present a bootstrapping algorithm that uses constrained discovery to improve generation, and can be used with as few as 500 training examples, which we show can be sourced from annotators in a matter of hours. This opens the task to languages for which large number of training examples are unavailable. We evaluate transliteration generation performance itself, as well the improvement it brings to cross-lingual candidate generation for entity linking, a typical downstream task. We present a comprehensive evaluation of our approach on nine languages, each written in a unique script.

Connected through hyperedge HE-1 (induced by source paper "improving candidate generation for low resource cross lingual entity linking"):
  with members:
    - "towards zero resource cross lingual entity linking"
    - "massively multilingual transfer for ner"
    - "choosing transfer languages for cross lingual learning"
    - "joint multilingual supervision for cross lingual entity linking"
    - "unsupervised multilingual word embeddings"
    - "zero shot neural transfer for cross lingual entity linking"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "one for all towards language independent named entity linking"

Connected through hyperedge HE-2 (induced by source paper "towards realistic practices in low resource natural language processing the development set"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "diverse few shot text classification with multiple metrics"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "joint multilingual supervision for cross lingual entity linking"):
  with members:
    - "deeptype multilingual entity linking by neural type system evolution"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "enriching word vectors with subword information"
    - "massively multilingual 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)