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: "enhancing and combining sequential and tree lstm for natural language inference"
  Abstract: Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is notoriously challenging but is fundamental to natural language understanding and many applications. With the availability of large annotated data, neural network models have recently advanced the field significantly. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.3% on the standard benchmark, the Stanford Natural Language Inference dataset. This result is achieved first through our enhanced sequential encoding model, which outperforms the previous best model that employs more complicated network architectures, suggesting that the potential of sequential LSTM-based models have not been fully explored yet in previous work. We further show that by explicitly considering recursive architectures, we achieve additional improvement. Particularly, incorporating syntactic parse information contributes to our best result; it improves the performance even when the parse information is added to an already very strong system.

Connected through hyperedge HE-1 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "on first order meta learning algorithms"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-2 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "transforming question answering datasets into natural language inference datasets"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "large scale qa srl parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-3 (induced by source paper "bert for evidence retrieval and claim verification"):
  with members:
    - "reasoning over semantic level graph for fact checking"
    - "team papelo transformer networks at fever"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-4 (induced by source paper "rethinking self attention an interpretable self attentive encoder decoder parser"):
  with members:
    - "fine tune bert for extractive summarization"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "impact of corpora quality on neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "probing neural network comprehension of natural language arguments"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nlitrans at semeval 2018 task 12 transfer of semantic knowledge for argument comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "fake news detection as natural language inference"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "language modeling with gated convolutional networks"
    - "enriching word vectors with subword information"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-7 (induced by source paper "suffix bidirectional long short term memory"):
  with members:
    - "an efficient framework for learning sentence representations"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning to generate reviews and discovering sentiment"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "natural language inference over interaction space"
    - "lstm a search space odyssey"

Connected through hyperedge HE-8 (induced by source paper "a better way to attend attention with trees for video question answering"):
  with members:
    - "tree structured reinforcement learning for sequential object localization"
    - "tree to sequence attentional neural machine translation"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"
    - "enhanced lstm for natural language inference"
    - "video fill in the blank with merging lstms"
    - "hierarchical question image co attention for visual question answering"
    - "tgif a new dataset and benchmark on animated gif description"

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)