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: "comprehensive and reliable crowd assessment algorithms"
  Abstract: Evaluating workers is a critical aspect of any crowdsourcing system. In this paper, we devise techniques for evaluating workers by finding confidence intervals on their error rates. Unlike prior work, we focus on "conciseness"---that is, giving as tight a confidence interval as possible. Conciseness is of utmost importance because it allows us to be sure that we have the best guarantee possible on worker error rate. Also unlike prior work, we provide techniques that work under very general scenarios, such as when not all workers have attempted every task (a fairly common scenario in practice), when tasks have non-boolean responses, and when workers have different biases for positive and negative tasks. We demonstrate conciseness as well as accuracy of our confidence intervals by testing them on a variety of conditions and multiple real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "metrology for ai from benchmarks to instruments"):
  with members:
    - "discovering blind spots in reinforcement learning"
    - "crowdsourcing ground truth for medical relation extraction"
    - "factors influencing the surprising instability of word embeddings"
    - "enriching word vectors with subword information"
    - "exposing ambiguities in a relation extraction gold standard with crowdsourcing"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (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"
    - "finish them pricing algorithms for human computation"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-3 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "fonduer knowledge base construction from richly formatted data"
    - "training classifiers with natural language explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "inferring generative model structure with static analysis"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning the structure of generative models without labeled data"
    - "label free supervision of neural networks with physics and domain knowledge"

Connected through hyperedge HE-4 (induced by source paper "learning the structure of generative models without labeled data"):
  with members:
    - "incremental knowledge base construction using deepdive"
    - "dimmwitted a study of main memory statistical analytics"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-5 (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"
    - "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"
    - "human assisted graph search it s okay to ask questions"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-6 (induced by source paper "slimfast guaranteed results for data fusion and source reliability"):
  with members:
    - "from data fusion to knowledge fusion"
    - "incremental knowledge base construction using deepdive"
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "dimmwitted a study of main memory statistical analytics"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"
    - "optimization with sparsity inducing penalties"

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)