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: "modeling relational information in question answer pairs with convolutional neural networks"
  Abstract: In this paper, we propose convolutional neural networks for learning an optimal representation of question and answer sentences. Their main aspect is the use of relational information given by the matches between words from the two members of the pair. The matches are encoded as embeddings with additional parameters (dimensions), which are tuned by the network. These allows for better capturing interactions between questions and answers, resulting in a significant boost in accuracy. We test our models on two widely used answer sentence selection benchmarks. The results clearly show the effectiveness of our relational information, which allows our relatively simple network to approach the state of the art.

Connected through hyperedge HE-1 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection"):
  with members:
    - "passage re ranking with bert"
    - "tanda transfer and adapt pre trained transformer models for answer sentence selection"
    - "understanding the behaviors of bert in ranking"
    - "a gated self attention memory network for answer selection"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "hyperbolic representation learning for fast and efficient neural question answering"

Connected through hyperedge HE-2 (induced by source paper "tanda transfer and adapt pre trained transformer models for answer sentence selection"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "transformer xl attentive language models beyond a fixed length context"
    - "roberta a robustly optimized bert pretraining approach"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "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"
    - "multi cast attention networks for retrieval based question answering and response prediction"

Connected through hyperedge HE-3 (induced by source paper "adversarial sampling and training for semi supervised information retrieval"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "adversarial training for community question answer selection based on multi scale matching"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "interpretable convolutional neural networks"
    - "attentive pooling networks"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-4 (induced by source paper "a quantum many body wave function inspired language modeling approach"):
  with members:
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural ranking models with weak supervision"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "attentive pooling networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "character aware neural language models"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-5 (induced by source paper "injecting relational structural representation in neural networks for question similarity"):
  with members:
    - "bilateral multi perspective matching for natural language sentences"
    - "squad 100 000 questions for machine comprehension of text"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "text coherence analysis based on deep neural network"):
  with members:
    - "open question answering with weakly supervised embedding models"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "catching the drift probabilistic content models with applications to generation and summarization"

Connected through hyperedge HE-7 (induced by source paper "natural language comprehension with the epireader"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "text understanding with the attention sum reader network"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "learning natural language inference with lstm"
    - "teaching machines to read and comprehend"
    - "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)