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 multimodal alerting system for online class quality assurance"
  Abstract: Online 1 on 1 class is created for more personalized learning experience. It demands a large number of teaching resources, which are scarce in China. To alleviate this problem, we build a platform (marketplace), i.e., \emph{Dahai} to allow college students from top Chinese universities to register as part-time instructors for the online 1 on 1 classes. To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment. Our system mainly consists of two key components: banned word detector and class quality predictor. The system performance is demonstrated both offline and online. By conducting experimental evaluation of real-world online courses, we are able to achieve 74.3\% alerting accuracy in our production environment.

Connected through hyperedge HE-1 (induced by source paper "dolphin a verbal fluency evaluation system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"
    - "disfluency detection using a bidirectional lstm"

Connected through hyperedge HE-2 (induced by source paper "dolphin a spoken language proficiency assessment system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"
    - "disfluency detection using a bidirectional lstm"

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.HC(Human-Computer Interaction)