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: "mobile crowd sensing and computing when participatory sensing meets participatory social media"
  Abstract: With the development of mobile sensing and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characterizing features: (1) it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and (2) it presents the fusion of human and machine intelligence (HMI) in both the sensing and computing process. This paper characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

Connected through hyperedge HE-1 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "trust evaluation mechanism for user recruitment in mobile crowd sensing in the internet of things"):
  with members:
    - "social network assisted worker recruitment in mobile crowd sensing"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "when crowdsourcing meets mobile sensing a social network perspective"
    - "from participatory sensing to mobile crowd sensing"
    - "sensing as a service model for smart cities supported by internet of things"
    - "internet of things iot a vision architectural elements and future directions"
    - "pagerank standing on the shoulders of giants"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "stigmergic independent reinforcement learning for multi agent collaboration"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "prioritized experience replay"
    - "the arcade learning environment an evaluation platform for general agents"
    - "antnet distributed stigmergetic control for communications networks"

Connected through hyperedge HE-4 (induced by source paper "big data meet cyber physical systems a panoramic survey"):
  with members:
    - "information and communications technologies for sustainable development goals state of the art needs and perspectives"
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "developing an edge computing platform for real time descriptive analytics"
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "wireless device to device caching networks basic principles and system performance"
    - "social computing for mobile big data"
    - "streaming big data meets backpressure in distributed network computation"
    - "big data caching for networking moving from cloud to edge"

Connected through hyperedge HE-5 (induced by source paper "wireless communications in the era of big data"):
  with members:
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "min flow rate maximization for software defined radio access networks"
    - "exploiting opportunistic multiuser detection in decentralized multiuser mimo systems"

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)