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: "neural cross lingual entity linking"
  Abstract: A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cross lingual dependency parsing with unlabeled auxiliary languages"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "cross lingual syntactic transfer through unsupervised adaptation of invertible projections"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial neural networks for cross lingual sequence tagging"
    - "self attention with relative position representations"

Connected through hyperedge HE-4 (induced by source paper "rewarding smatch transition based amr parsing with reinforcement learning"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "amr parsing using stack lstms"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-5 (induced by source paper "yelm end to end contextualized entity linking"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"
    - "end to end neural entity linking"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "attention is all you need"
    - "learning distributed representations of texts and entities from knowledge base"

Connected through hyperedge HE-6 (induced by source paper "question answering based clinical text structuring using pre trained language model"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "deep contextualized word representations"
    - "neural semi markov conditional random fields for robust character based part of speech tagging"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-7 (induced by source paper "zero shot entity linking with dense entity retrieval"):
  with members:
    - "learning dense representations for entity retrieval"
    - "fine grained entity typing for domain independent entity linking"
    - "zero shot entity linking by reading entity descriptions"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "pytorch biggraph a large scale graph embedding system"
    - "neural collective entity linking"
    - "scalable graph based individual named entity identification"
    - "end to end neural entity linking"

Connected through hyperedge HE-8 (induced by source paper "learning dense representations for entity retrieval"):
  with members:
    - "end to end retrieval in continuous space"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient natural language response suggestion for smart reply"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"
    - "dynamic entity representations in neural language models"
    - "capturing semantic similarity for entity linking with convolutional neural networks"

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)