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: "prediction based task assignment in spatial crowdsourcing"
  Abstract: With the rapid advancement of mobile devices and crowdsourcing platforms, spatial crowdsourcing has attracted much attention from various research communities. A spatial crowdsourcing system periodically matches a number of locationbased workers with nearby spatial tasks (e.g., taking photos or videos at some specific locations). Previous studies on spatial crowdsourcing focus on task assignment strategies that maximize an assignment score based solely on the available information about workers/tasks at the time of assignment. These strategies can only achieve local optimality by neglecting the workers/tasks that may join the system in a future time. In contrast, in this paper, we aim to improve the global assignment, by considering both present and future (via predictions) workers/tasks. In particular, we formalize a new optimization problem, namely maximum quality task assignment (MQA). The optimization objective of MQA is to maximize a global assignment quality score, under a traveling budget constraint. To tackle this problem, we design an effective grid-based prediction method to estimate the spatial distributions of workers/tasks in the future, and then utilize the predictions to assign workers to tasks at any given time instance. We prove that the MQA problem is NPhard, and thus intractable. Therefore, we propose efficient heuristics to tackle the MQA problem, including MQA greedy and MQA divide-and-conquer approaches, which can efficiently assign workers to spatial tasks with high quality scores and low budget consumptions. Through extensive experiments, we demonstrate the efficiency and effectiveness of our approaches on both real and synthetic datasets.

Connected through hyperedge HE-1 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "taskme multi task allocation in mobile crowd sensing"
    - "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-2 (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"
    - "taskme multi task allocation in mobile crowd sensing"
    - "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-3 (induced by source paper "budget aware online task assignment in spatial crowdsourcing"):
  with members:
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "fundamental limits of budget fidelity trade off in label crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "prediction based task assignment on spatial crowdsourcing"):
  with members:
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"

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.DB(Databases)