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: "hiring doctors in e healthcare with zero budget"
  Abstract: The doctors (or expert consultants) are the critical resources on which the success of critical medical cases are heavily dependent. With the emerging technologies (such as video conferencing, smartphone, etc.) this is no longer a dream but a fact, that for critical medical cases in a hospital, expert consultants from around the world could be hired, who may be present physically or virtually. Earlier, this interesting situation by taking the expert consultancies from outside the hospital had been studied, but under monetary perspective. In this paper, for the first time, to the best of our knowledge, we investigate the situation, where the below income group (BIG) people of the society may be served efficiently through the expert consultancy by the renowned doctors from outside of the hospital under zero budget. This will help us saving many lives which will fulfil the present day need of biomedical research. We propose three mechanisms: Random pick-assign mechanism (RanPAM), Truthful optimal allocation mechanism (TOAM), and Truthful optimal allocation mechanism for incomplete preferences (TOAM-IComP) to allocate the doctor to the patient. With theoretical analysis, we demonstrate that the TOAM is strategy-proof, and exhibits a unique core property. The mechanisms are also validated with exhaustive experiments.

Connected through hyperedge HE-1 (induced by source paper "hiring expert consultants in e healthcare an analytics based two sided matching approach"):
  with members:
    - "hiring expert consultants in e healthcare with budget constraint"
    - "mechanism design without money via stable matching"
    - "truthful assignment without money"

Connected through hyperedge HE-2 (induced by source paper "hiring expert consultants in e healthcare with budget constraint"):
  with members:
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "on cost effective incentive mechanisms in microtask crowdsourcing"
    - "participatory privacy enabling privacy in participatory sensing"
    - "budget feasible mechanisms"

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.GT(Computer Science and Game Theory)