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: "ordered neurons integrating tree structures into recurrent neural networks"
  Abstract: Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

Connected through hyperedge HE-1 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "inducing syntactic trees from bert representations"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "compound probabilistic context free grammars for grammar induction"
    - "from balustrades to pierre vinken looking for syntax in transformer self attentions"
    - "assessing bert s syntactic abilities"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"

Connected through hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "r transformer recurrent neural network enhanced transformer"
    - "learning to remember more with less memorization"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "jiant a software toolkit for research on general purpose text understanding models"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "albert a lite bert for self supervised learning of language representations"
    - "probing what different nlp tasks teach machines about function word comprehension"

Connected through hyperedge HE-4 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "a critical analysis of biased parsers in unsupervised parsing"
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "tree to tree neural networks for program translation"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-5 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model"):
  with members:
    - "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a large self annotated corpus for sarcasm"
    - "convolutional sequence to sequence learning"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-6 (induced by source paper "hybrid neural tagging model for open relation extraction"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "graph neural networks with generated parameters for relation extraction"
    - "openki integrating open information extraction and knowledge bases with relation inference"
    - "design challenges and misconceptions in neural sequence labeling"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "deep active learning for named entity recognition"
    - "neural models for sequence chunking"

Connected through hyperedge HE-7 (induced by source paper "tree structured attention with hierarchical accumulation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "tree transformer a transformer based method for correction of tree structured data"
    - "pay less attention with lightweight and dynamic convolutions"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "on tree based neural sentence modeling"
    - "self attention with relative position representations"
    - "scaling neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "stagenet stage aware neural networks for health risk prediction"):
  with members:
    - "patient subtyping with disease progression and irregular observation trajectories"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "adam a method for stochastic optimization"

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)