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: "active learning for crowd sourced databases"
  Abstract: Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objects, or analyzing sentiment. However, relying solely on the crowd is often impractical even for data sets with thousands of items, due to time and cost constraints of acquiring human input (which cost pennies and minutes per label). In this paper, we propose algorithms for integrating machine learning into crowd-sourced databases, with the goal of allowing crowd-sourcing applications to scale, i.e., to handle larger datasets at lower costs. The key observation is that, in many of the above tasks, humans and machine learning algorithms can be complementary, as humans are often more accurate but slow and expensive, while algorithms are usually less accurate, but faster and cheaper. #R##N#Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database. Our algorithms are based on the theory of non-parametric bootstrap, which makes our results applicable to a broad class of machine learning models. Our results, on three real-life datasets collected with Amazon's Mechanical Turk, and on 15 well-known UCI data sets, show that our methods on average ask humans to label one to two orders of magnitude fewer items to achieve the same accuracy as a baseline that labels random images, and two to eight times fewer questions than previous active learning schemes.

Connected through hyperedge HE-1 (induced by source paper "reducing uncertainty of schema matching via crowdsourcing with accuracy rates"):
  with members:
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "mining frequent itemsets over uncertain databases"
    - "human assisted graph search it s okay to ask questions"
    - "human powered sorts and joins"

Connected through hyperedge HE-2 (induced by source paper "gaussian process classification for variable fidelity data"):
  with members:
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "a survey on multi task learning"
    - "crowd prejudice an impossibility theorem for crowd labelling without a gold standard"

Connected through hyperedge HE-3 (induced by source paper "distinguishing question subjectivity from difficulty for improved crowdsourcing"):
  with members:
    - "crowdsourced task routing via matrix factorization"
    - "crowder crowdsourcing entity resolution"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"

Connected through hyperedge HE-4 (induced by source paper "crowdfusion a crowdsourced approach on data fusion refinement"):
  with members:
    - "fusing data with correlations"
    - "from data fusion to knowledge fusion"
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "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.LG(Machine Learning)