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 natural language inference models enhanced with external knowledge"
  Abstract: Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.

Connected through hyperedge HE-1 (induced by source paper "sequential neural networks for noetic end to end response selection"):
  with members:
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "sequential attention based network for noetic end to end response selection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhancing sentence embedding with generalized pooling"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "enhancing sentence embedding with generalized pooling"
    - "reinforced mnemonic reader for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "molecule attention transformer"):
  with members:
    - "alchemy a quantum chemistry dataset for benchmarking ai models"
    - "do attention heads in bert track syntactic dependencies"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "enhanced deep learning prediction of molecular properties via augmentation of bond topology"
    - "self attention based molecule representation for predicting drug target interaction"
    - "what does bert look at an analysis of bert s attention"
    - "adaptive graph convolutional neural networks"
    - "potentialnet for molecular property prediction"

Connected through hyperedge HE-4 (induced by source paper "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "a cross sentence latent variable model for semi supervised text sequence matching"):
  with members:
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generating sentences by editing prototypes"
    - "deconvolutional latent variable model for text sequence matching"
    - "adversarially regularized autoencoders"

Connected through hyperedge HE-6 (induced by source paper "what s missing a knowledge gap guided approach for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "careful selection of knowledge to solve open book question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"

Connected through hyperedge HE-7 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"

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)