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 compare aggregate model with latent clustering for answer selection"
  Abstract: In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing. First, we explore the effect of additional information by adopting a pretrained language model to compute the vector representation of the input text and by applying transfer learning from a large-scale corpus. Second, we enhance the compare-aggregate model by proposing a novel latent clustering method to compute additional information within the target corpus and by changing the objective function from listwise to pointwise. To evaluate the performance of the proposed approaches, experiments are performed with the WikiQA and TREC-QA datasets. The empirical results demonstrate the superiority of our proposed approach, which achieve state-of-the-art performance for both datasets.

Connected through hyperedge HE-1 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey"):
  with members:
    - "bertsel answer selection with pre trained models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sanity check a strong alignment and information retrieval baseline for question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-2 (induced by source paper "propagate selector detecting supporting sentences for question answering via graph neural networks"):
  with members:
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph neural networks for social recommendation"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "deep contextualized word representations"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "tanda transfer and adapt pre trained transformer models for answer sentence selection"):
  with members:
    - "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"
    - "question answering through transfer learning from large fine grained supervision data"

Connected through hyperedge HE-4 (induced by source paper "a gated self attention memory network for answer selection"):
  with members:
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "supervised and unsupervised transfer learning for question answering"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-5 (induced by source paper "bas an answer selection method using bert language model"):
  with members:
    - "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"
    - "albert a lite bert for self supervised learning of language representations"
    - "deep learning using rectified linear units relu"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "recurrent additive networks"
    - "exploring the effectiveness of convolutional neural networks for answer selection in end to end question answering"
    - "applying deep learning to answer selection a study and an open task"

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)