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: "taskme multi task allocation in mobile crowd sensing"
  Abstract: Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).

Connected through hyperedge HE-1 (induced by source paper "crowdos a ubiquitous operating system for crowdsourcing and mobile crowd sensing"):
  with members:
    - "first a framework for optimizing information quality in mobile crowdsensing systems"

Connected through hyperedge HE-2 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities"):
  with members:
    - "prediction based task assignment in spatial crowdsourcing"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "incentives for privacy tradeoff in community sensing"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-3 (induced by source paper "hytasker hybrid task allocation in mobile crowd sensing"):
  with members:
    - "task allocation in mobile crowd sensing state of the art and future opportunities"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "prediction based task assignment in spatial crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "lazier than lazy greedy"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-4 (induced by source paper "incentme effective mechanism design to stimulate crowdsensing participants with uncertain mobility"):
  with members:
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "mining users significant driving routes with low power sensors"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

Connected through hyperedge HE-5 (induced by source paper "intelligentcrowd mobile crowdsensing via multi agent reinforcement learning"):
  with members:
    - "energy storage arbitrage in real time markets via reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (induced by source paper "social network assisted worker recruitment in mobile crowd sensing"):
  with members:
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "incentives for privacy tradeoff in community sensing"

Connected through hyperedge HE-7 (induced by source paper "first a framework for optimizing information quality in mobile crowdsensing systems"):
  with members:
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "ear phone a context aware noise mapping using smart phones"

Connected through hyperedge HE-8 (induced by source paper "quality of information in mobile crowdsensing survey and research challenges"):
  with members:
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "sacrm social aware crowdsourcing with reputation management in mobile sensing"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "a survey on truth discovery"
    - "mining users significant driving routes with low power sensors"
    - "ear phone a context aware noise mapping using smart phones"
    - "a trust based recruitment framework for multi hop social participatory sensing"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

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)