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 deep learning approach for expert identification in question answering communities"
  Abstract: In this paper, we describe an effective convolutional neural network framework for identifying the expert in question answering community. This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the highest score is the expert on this question. Unlike prior work, this method does not measure expert based on measure answer content quality to identify the expert but only require question sentence and user embedding feature to identify the expert. Remarkably, Our model can be applied to different languages and different domains. The proposed framework is trained on two datasets, The first dataset is Stack Overflow and the second one is Zhihu. The Top-1 accuracy results of our experiments show that our framework outperforms the best baseline framework for expert identification.

Connected through hyperedge HE-1 (induced by source paper "from vqa to multimodal cqa adapting visual qa models for community qa tasks"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "image captioning with semantic attention"

Connected through hyperedge HE-2 (induced by source paper "adapting visual question answering models for enhancing multimodal community q a platforms"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "image captioning with semantic attention"

Connected through hyperedge HE-3 (induced by source paper "a survey on expert recommendation in community question answering"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "characterization of experts in crowdsourcing platforms"
    - "co authorship networks in the digital library research community"
    - "optimal aggregation algorithms for middleware"

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)