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: "self adaptive hierarchical sentence model"
  Abstract: The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-3 (induced by source paper "graph u nets"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "large scale learnable graph convolutional networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "predicting multicellular function through multi layer tissue networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "efficient and invariant convolutional neural networks for dense prediction"

Connected through hyperedge HE-4 (induced by source paper "structure tree lstm structure aware attentional document encoders"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "neural document embeddings for intensive care patient mortality prediction"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "interpretable structure aware document encoders with hierarchical attention"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "neural document embeddings for intensive care patient mortality prediction"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "vector of locally aggregated word embeddings vlawe a novel document level representation"):
  with members:
    - "automated essay scoring with string kernels and word embeddings"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "from image to text classification a novel approach based on clustering word embeddings"
    - "dynamic compositional neural networks over tree structure"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-7 (induced by source paper "multi granular text encoding for self explaining categorization"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "rationalizing neural predictions"
    - "bidirectional tree structured lstm with head lexicalization"
    - "visualizing and understanding neural models in nlp"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "multi task learning for universal sentence representations what syntactic and semantic information is captured"):
  with members:
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "end to end neural coreference resolution"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "a generalized recurrent neural architecture for text classification with multi task learning"

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)