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: "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"
  Abstract: Semantic similarity based retrieval is playing an increasingly important role in many IR systems such as modern web search, question-answering, similar document retrieval etc. Improvements in retrieval of semantically similar content are very significant to applications like Quora, Stack Overflow, Siri etc. We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model.   We demonstrate the effectiveness of our model on an equivalent question retrieval problem on the Stack Exchange QA dataset, where our unsupervised approach significantly outperforms the state-of-the-art unsupervised models, and produces comparable results to the best supervised models. Our research provides a method to tackle semantic similarity based retrieval without any training data, and allows seamless extension to different domain QA communities, as well as to other semantic equivalence tasks.

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"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "a deep learning approach for expert identification in question answering communities"
    - "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"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "a deep learning approach for expert identification in question answering communities"
    - "image captioning with semantic attention"

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.IR(Information Retrieval)