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: "joint representation learning of cross lingual words and entities via attentive distant supervision"
  Abstract: Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.

Connected through hyperedge HE-1 (induced by source paper "is aligning embedding spaces a challenging task an analysis of the existing methods"):
  with members:
    - "word translation without parallel data"
    - "joint representation learning of text and knowledge for knowledge graph completion"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "language models as knowledge bases"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "multi task deep neural networks for natural language understanding"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-3 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "improving question answering by commonsense based pre training"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-4 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "neural collective entity linking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "visually explainable recommendation"

Connected through hyperedge HE-5 (induced by source paper "kgat knowledge graph attention network for recommendation"):
  with members:
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "neural graph collaborative filtering"
    - "neural collective entity linking"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "representation learning on graphs with jumping knowledge networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "low resource name tagging learned with weakly labeled data"):
  with members:
    - "few shot classification in named entity recognition task"
    - "improving neural relation extraction with implicit mutual relations"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "multi channel graph neural network for entity alignment"
    - "neural collective entity linking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "few shot learning for named entity recognition in medical text"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "k bert enabling language representation with knowledge graph"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "pre training with whole word masking for chinese bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (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"
    - "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"
    - "named entity disambiguation for noisy text"

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)