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: "whom to ask jury selection for decision making tasks on micro blog services"
  Abstract: It is universal to see people obtain knowledge on micro-blog services by asking others decision making questions. In this paper, we study the Jury Selection Problem(JSP) by utilizing crowdsourcing for decision making tasks on micro-blog services. Specifically, the problem is to enroll a subset of crowd under a limited budget, whose aggregated wisdom via Majority Voting scheme has the lowest probability of drawing a wrong answer(Jury Error Rate-JER). Due to various individual error-rates of the crowd, the calculation of JER is non-trivial. Firstly, we explicitly state that JER is the probability when the number of wrong jurors is larger than half of the size of a jury. To avoid the exponentially increasing calculation of JER, we propose two efficient algorithms and an effective bounding technique. Furthermore, we study the Jury Selection Problem on two crowdsourcing models, one is for altruistic users(AltrM) and the other is for incentive-requiring users(PayM) who require extra payment when enrolled into a task. For the AltrM model, we prove the monotonicity of JER on individual error rate and propose an efficient exact algorithm for JSP. For the PayM model, we prove the NP-hardness of JSP on PayM and propose an efficient greedy-based heuristic algorithm. Finally, we conduct a series of experiments to investigate the traits of JSP, and validate the efficiency and effectiveness of our proposed algorithms on both synthetic and real micro-blog data.

Connected through hyperedge HE-1 (induced by source paper "crowd powered data mining"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi label annotation aggregation in crowdsourcing"
    - "clustering via crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "comprehensive and reliable crowd assessment algorithms"
    - "finish them pricing algorithms for human computation"
    - "leveraging transitive relations for crowdsourced joins"

Connected through hyperedge HE-2 (induced by source paper "truth inference on sparse crowdsourcing data with local differential privacy"):
  with members:
    - "practical locally private heavy hitters"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "extremal mechanisms for local differential privacy"
    - "cdas a crowdsourcing data analytics system"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "select your questions wisely for entity resolution with crowd errors"):
  with members:
    - "leveraging transitive relations for crowdsourced joins"
    - "evaluating the crowd with confidence"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "human assisted graph search it s okay to ask questions"
    - "human powered sorts and joins"

Connected through hyperedge HE-4 (induced by source paper "frog a fast and reliable crowdsourcing framework technical report"):
  with members:
    - "octopus a framework for cost quality time optimization in crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "finish them pricing algorithms for human computation"
    - "crowdsourcing a word emotion association lexicon"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "tuning crowdsourced human computation"):
  with members:
    - "cdas a crowdsourcing data analytics system"
    - "time critical social mobilization"

Connected through hyperedge HE-6 (induced by source paper "task assignment on multi skill oriented spatial crowdsourcing"):
  with members:
    - "reliable diversity based spatial crowdsourcing by moving workers"

Connected through hyperedge HE-7 (induced by source paper "crowdplanner a crowd based route recommendation system"):
  with members:
    - "crowder crowdsourcing entity resolution"
    - "human assisted graph search it s okay to ask questions"

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)