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: "cdas a crowdsourcing data analytics system"
  Abstract: Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectiv...[truncated].

Connected through hyperedge HE-1 (induced by source paper "the disruptions of 5g on data driven technologies and applications"):
  with members:
    - "an e2e simulator for 5g nr networks"
    - "federated machine learning concept and applications"
    - "blockchain goes green an analysis of blockchain on low power nodes"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "attentive geo social group recommendation"):
  with members:
    - "attention based group recommendation"
    - "interact and decide medley of sub attention networks for effective group recommendation"
    - "on social temporal group query with acquaintance constraint"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-3 (induced by source paper "graph mining meets crowdsourcing extracting experts for answer aggregation"):
  with members:
    - "the densest subgraph problem with a convex concave size function"
    - "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 "attention aware answers of the crowd"):
  with members:
    - "incentivizing high quality crowdwork"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "deep forest"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"

Connected through hyperedge HE-6 (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-7 (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"
    - "calibrating data to sensitivity in private data analysis"
    - "whom to ask jury selection for decision making tasks on micro blog services"

Connected through hyperedge HE-8 (induced by source paper "reducing uncertainty of schema matching via crowdsourcing with accuracy rates"):
  with members:
    - "crowder crowdsourcing entity resolution"
    - "mining frequent itemsets over uncertain databases"
    - "active learning for crowd sourced databases"
    - "human assisted graph search it s okay to ask questions"
    - "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)