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: "discriminative neural sentence modeling by tree based convolution"
  Abstract: This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our models leverage either constituency trees or dependency trees of sentences. The tree-based convolution process extracts sentences' structural features, and these features are aggregated by max pooling. Such architecture allows short propagation paths between the output layer and underlying feature detectors, which enables effective structural feature learning and extraction. We evaluate our models on two tasks: sentiment analysis and question classification. In both experiments, TBCNN outperforms previous state-of-the-art results, including existing neural networks and dedicated feature/rule engineering. We also make efforts to visualize the tree-based convolution process, shedding light on how our models work.

Connected through hyperedge HE-1 (induced by source paper "topic sensitive attention on generic corpora corrects sense bias in pretrained embeddings"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-2 (induced by source paper "fine tuned language models for text classification"):
  with members:
    - "deep contextualized word representations"
    - "colorless green recurrent networks dream hierarchically"
    - "learning to generate reviews and discovering sentiment"
    - "question answering through transfer learning from large fine grained supervision data"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-3 (induced by source paper "dynamic compositionality in recursive neural networks with structure aware tag representations"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "enhancing sentence embedding with generalized pooling"
    - "learning to generate reviews and discovering sentiment"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "learned in translation contextualized word vectors"
    - "decoupled weight decay regularization"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-4 (induced by source paper "learning to compose over tree structures via pos tags"):
  with members:
    - "learned in translation contextualized word vectors"
    - "dynamic compositional neural networks over tree structure"
    - "a structured self attentive sentence embedding"
    - "learning feed forward one shot learners"
    - "recurrent dropout without memory loss"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "neural semantic encoders"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-5 (induced by source paper "learned in translation contextualized word vectors"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "question answering through transfer learning from large fine grained supervision data"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning with dynamic computation graphs"
    - "opennmt open source toolkit for neural machine translation"
    - "end to end multi view networks for text classification"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "language to logical form with neural attention"

Connected through hyperedge HE-6 (induced by source paper "a structured self attentive sentence embedding"):
  with members:
    - "attentive pooling networks"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "a fast unified model for parsing and sentence understanding"
    - "learning distributed representations of sentences from unlabelled data"
    - "neural tree indexers for text understanding"

Connected through hyperedge HE-7 (induced by source paper "encoding sentences with graph convolutional networks for semantic role labeling"):
  with members:
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "learning to parse and translate improves neural machine translation"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "language modeling with gated convolutional networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural semantic role labeling with dependency path embeddings"
    - "linguistic input features improve neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "neural attribute machines for program generation"):
  with members:
    - "neural sketch learning for conditional program generation"
    - "a neural representation of sketch drawings"
    - "deepcoder learning to write programs"
    - "pixel recurrent neural networks"
    - "neuro symbolic program synthesis"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "learning to transduce with unbounded memory"
    - "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)