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: "a fast unified model for parsing and sentence understanding"
  Abstract: Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suffer from two key technical problems that make them slow and unwieldy for large-scale NLP tasks: they usually operate on parsed sentences and they do not directly support batched computation. We address these issues by introducing the Stack-augmented Parser-Interpreter Neural Network (SPINN), which combines parsing and interpretation within a single tree-sequence hybrid model by integrating tree-structured sentence interpretation into the linear sequential structure of a shift-reduce parser. Our model supports batched computation for a speedup of up to 25 times over other tree-structured models, and its integrated parser can operate on unparsed data with little loss in accuracy. We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "deepenroll patient trial matching with deep embedding and entailment prediction"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "publicly available clinical bert embeddings"
    - "learning eligibility in cancer clinical trials using deep neural networks"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "identity aware textual visual matching with latent co attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "sg net syntax guided machine reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "limit bert linguistic informed multi task bert"
    - "syntax aware neural semantic role labeling with supertags"
    - "global greedy dependency parsing"
    - "option comparison network for multiple choice reading comprehension"
    - "probing contextualized sentence representations with visual awareness"
    - "what does bert look at an analysis of bert s attention"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-7 (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"
    - "neural natural language inference models enhanced with external knowledge"
    - "deconvolutional latent variable model for text sequence matching"

Connected through hyperedge HE-8 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

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)