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: "building language models for text with named entities"
  Abstract: Text in many domains involves a significant amount of named entities. Predict- ing the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a discriminative language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evalu- ate the proposed model. Experimental re- sults show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than the state-of-the-art language models.

Connected through hyperedge HE-1 (induced by source paper "latent relation language models"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "bootstrapping generators from noisy data"
    - "neural lattice language models"
    - "describing a knowledge base"
    - "deep contextualized word representations"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning to compute word embeddings on the fly"

Connected through hyperedge HE-2 (induced by source paper "strategies for structuring story generation"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "generating wikipedia by summarizing long sequences"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "higher order coreference resolution with coarse to fine inference"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "knowledge augmented language model and its application to unsupervised named entity recognition"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "language modeling with sparse product of sememe experts"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence tagging with bidirectional language models"
    - "regularizing and optimizing lstm language models"
    - "a neural knowledge language model"
    - "reference aware language models"

Connected through hyperedge HE-4 (induced by source paper "codit code editing with tree based neuralmachine translation"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "learning to represent edits"
    - "tree to tree neural networks for program translation"
    - "opennmt open source toolkit for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-5 (induced by source paper "on the generation of medical question answer pairs"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "deep contextualized word representations"
    - "emrqa a large corpus for question answering on electronic medical records"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "attention over attention neural networks for reading comprehension"

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)