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: "oralcam enabling self examination and awareness of oral health using a smartphone camera"
  Abstract: Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-cost, sensor-equipped smartphones, mobile apps offer a promising possibility for promoting oral health. However, to the best of our knowledge, no mobile health (mHealth) solutions can directly support a user to self-examine their oral health condition. This paper presents OralCam, the first interactive app that enables end-users' self-examination of five common oral conditions (diseases or early disease signals) by taking smartphone photos of one's oral cavity. OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results. Developed on our in-house dataset that consists of 3,182 oral photos annotated by dental experts, our deep learning based framework achieved an average detection sensitivity of 0.787 over five conditions with high localization accuracy. In a week-long in-the-wild user study (N=18), most participants had no trouble using OralCam and interpreting the examination results. Two expert interviews further validate the feasibility of OralCam for promoting users' awareness of oral health.

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)