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: "analytic methods for optimizing realtime crowdsourcing"
  Abstract: Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.

Connected through hyperedge HE-1 (induced by source paper "auditing news curation systems a case study examining algorithmic and editorial logic in apple news"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-2 (induced by source paper "beyond monetary incentives experiments in paid microtask contests modelled as continuous time markov chains"):
  with members:
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-3 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"

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"
    - "whom to ask jury selection for decision making tasks on micro blog services"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "it s about time online macrotask sequencing in expert crowdsourcing"):
  with members:
    - "online assignment of heterogeneous tasks in crowdsourcing markets"
    - "engineering crowdsourced stream processing systems"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "clamshell speeding up crowds for low latency data labeling"):
  with members:
    - "mllib machine learning in apache spark"
    - "finish them pricing algorithms for human computation"
    - "crowder crowdsourcing entity resolution"
    - "human powered sorts and joins"
    - "active learning with statistical models"

Connected through hyperedge HE-7 (induced by source paper "optimization in knowledge intensive crowdsourcing"):
  with members:
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "human powered sorts and joins"

Connected through hyperedge HE-8 (induced by source paper "crowd labeling a survey"):
  with members:
    - "using mechanical turk to build machine translation evaluation sets"
    - "expectation propagation for approximate bayesian inference"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "budget optimal task allocation for reliable crowdsourcing 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.SI(Social and Information Networks)