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: "human assisted graph search it s okay to ask questions"
  Abstract: We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form "Is there a target node that is reachable from the current node?". This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. Our framework and algorithms can be used in the design of an optimizer for crowd-sourcing platforms such as Mechanical Turk.

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 "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"
    - "active learning for crowd sourced databases"
    - "human powered sorts and joins"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (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"
    - "whom to ask jury selection for decision making tasks on micro blog services"
    - "human powered sorts and joins"

Connected through hyperedge HE-5 (induced by source paper "top k querying of unknown values under order constraints"):
  with members:
    - "possible and certain answers for queries over order incomplete data"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "a fast and practical method to estimate volumes of convex polytopes"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "a unified approach to ranking in probabilistic databases"
    - "semantics and evaluation of top k queries in probabilistic databases"

Connected through hyperedge HE-6 (induced by source paper "optimizing open ended crowdsourcing the next frontier in crowdsourced data management"):
  with members:
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "a large annotated corpus for learning natural language inference"
    - "comprehensive and reliable crowd assessment algorithms"
    - "finish them pricing algorithms for human computation"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowdsourcing control moving beyond multiple choice"
    - "cdas a crowdsourcing data analytics system"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-7 (induced by source paper "it s just a matter of perspective s crowd powered consensus organization of corpora"):
  with members:
    - "learning concept embeddings with combined human machine expertise"
    - "building hierarchies of concepts via crowdsourcing"
    - "crowder crowdsourcing entity resolution"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-8 (induced by source paper "finish them pricing algorithms for human computation"):
  with members:
    - "evaluating the crowd with confidence"
    - "crowdsourcing control moving beyond multiple choice"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "human powered sorts and joins"

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)