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: "one for all towards language independent named entity linking"
  Abstract: Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other languages, such as Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent Entity Linking system, which provides an EL framework which, once trained on one language, works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document, employing a discriminative reranking framework with many domain and language-independent feature functions. Experiments on numerous benchmark datasets, show that the proposed system, once trained on one language, English, outperforms several state-of-the-art systems in English (by 4 points) and the trained model also works very well on Spanish (14 points better than a competitor system), demonstrating the viability of the approach.

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"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "zero shot neural transfer for cross lingual entity linking"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "learning in text streams discovery and disambiguation of entity and relation instances"):
  with members:
    - "relnet end to end modeling of entities relations"
    - "a neural knowledge language model"
    - "dynamic entity representations in neural language models"
    - "squad 100 000 questions for machine comprehension of text"
    - "tracking the world state with recurrent entity networks"
    - "neural architectures for named entity recognition"
    - "exploring the limits of language modeling"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-3 (induced by source paper "towards zero resource cross lingual entity linking"):
  with members:
    - "joint multilingual supervision for cross lingual entity linking"
    - "end to end neural entity linking"
    - "zero shot neural transfer for cross lingual entity linking"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "represent aggregate and constrain a novel architecture for machine reading from noisy sources"
    - "probabilistic bag of hyperlinks model for entity linking"

Connected through hyperedge HE-4 (induced by source paper "neural cross lingual coreference resolution and its application to entity linking"):
  with members:
    - "end to end neural coreference resolution"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "neural cross lingual entity linking"
    - "improving coreference resolution by learning entity level distributed representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "massively multilingual word embeddings"
    - "learning global features for coreference resolution"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-5 (induced by source paper "named entity linking using deep learning for legal documents a transfer learning approach"):
  with members:
    - "all transfer learning for deep neural networks and its application to sepsis classification"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-6 (induced by source paper "neural cross lingual entity linking"):
  with members:
    - "entity linking for queries by searching wikipedia sentences"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "neural networks models for entity discovery and linking"
    - "long short term memory networks for machine reading"
    - "sentence similarity learning by lexical decomposition and composition"
    - "massively multilingual word embeddings"
    - "multi perspective context matching for machine comprehension"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

Connected through hyperedge HE-7 (induced by source paper "mag a multilingual knowledge base agnostic and deterministic entity linking approach"):
  with members:
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "distributed representations of words and phrases and their compositionality"
    - "fast and accurate annotation of short texts with wikipedia pages"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)