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: "characterizing iomt personal area networks landscape"
  Abstract: The attention garnered by the Internet of Things (IoT) term has taken the world by a storm. IOT concepts have been applied to various domains revolutionizing the way business processes are conducted. Health Informatics is a nascent multidisciplinary field that aims at applying information engineering concepts to healthcare. The information traditionally came from a variety of sources such as healthcare IT systems but recently is being stored in variety of IOT devices. Application of IOT concepts is becoming the norm giving rise to the Internet of Medical Things (IOMT). However, IOMT introduces many challenges such as the real-time nature, security, and privacy of data along with others. Thus, understanding the underlying structure of IOMT is of paramount importance. Given the importance of the underlying networks, we explore various solutions currently employed in developing the Personal Area Networks (PANs) that are the underlying cornerstone of IOMT). PANs allow the sensors to measure the change in various stimuli and transmit the information via LANs and WANs to various stakeholders. This chapter will look at three standards that are currently used by both academia and industry along with Body Area Networks (BANs). The chapter also provides a survey of prevalent IOMT applications along with various vendors that provide such services.

Connected through hyperedge HE-1 (induced by source paper "clickbait in education positive or negative machine learning answers"):
  with members:
    - "natural language processing sentiment analysis and clinical analytics"
    - "making a case for social media corpus for detecting depression"
    - "byod personal area networks pans and iot threats to patients privacy"
    - "big data and social medical sciences state of the art and future trends"
    - "clickbait detection in tweets using self attentive network"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-2 (induced by source paper "byod personal area networks pans and iot threats to patients privacy"):
  with members:
    - "natural language processing sentiment analysis and clinical analytics"
    - "making a case for social media corpus for detecting depression"
    - "big data and social medical sciences state of the art and future trends"

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.CR(Cryptography and Security)