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: "reliable crowdsourcing for multi class labeling using coding theory"
  Abstract: Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding theory based techniques also allow us to pose easy-to-answer binary questions to the crowd workers. We consider three different crowdsourcing models: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, we analyze classification performance with the proposed coding-based scheme. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We also show that pairing among workers and diversification of the questions help in improving system performance. We demonstrate the effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches.

Connected through hyperedge HE-1 (induced by source paper "error correcting neural network"):
  with members:
    - "a mean field theory of batch normalization"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "batch normalization is a cause of adversarial vulnerability"
    - "adversarial attacks and defences competition"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "magnet a two pronged defense against adversarial examples"
    - "sequential multi class labeling in crowdsourcing"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-2 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "introduction to online convex optimization"
    - "bandit online learning with unknown delays"
    - "thompson sampling for combinatorial semi bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "sparsity variance and curvature in multi armed bandits"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "emerging privacy issues and solutions in cyber enabled sharing services from multiple perspectives"):
  with members:
    - "regulating highly automated robot ecologies insights from three user studies"
    - "information design in crowdfunding under thresholding policies"
    - "an online mechanism for ridesharing in autonomous mobility on demand systems"
    - "managing autonomous mobility on demand systems for better passenger experience"
    - "end to end privacy for open big data markets"
    - "the hidden cost of accommodating crowdfunder privacy preferences a randomized field experiment"
    - "a clustering based location privacy protection scheme for pervasive computing"

Connected through hyperedge HE-4 (induced by source paper "emerging privacy issues and solutions in cyber enabled sharing services from multiple perspectives"):
  with members:
    - "regulating highly automated robot ecologies insights from three user studies"
    - "information design in crowdfunding under thresholding policies"
    - "an online mechanism for ridesharing in autonomous mobility on demand systems"
    - "managing autonomous mobility on demand systems for better passenger experience"
    - "end to end privacy for open big data markets"
    - "the hidden cost of accommodating crowdfunder privacy preferences a randomized field experiment"
    - "a clustering based location privacy protection scheme for pervasive computing"

Connected through hyperedge HE-5 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers"):
  with members:
    - "decision tree design for classification in crowdsourcing systems"
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "multi object classification via crowdsourcing with a reject option"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-6 (induced by source paper "on multiple access systems with queue length dependent service quality"):
  with members:
    - "capacity of gaussian many access channels"
    - "capacity of systems with queue length dependent service quality"

Connected through hyperedge HE-7 (induced by source paper "decision tree design for classification in crowdsourcing systems"):
  with members:
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "multi object classification via crowdsourcing with a reject option"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-8 (induced by source paper "human machine inference networks for smart decision making opportunities and challenges"):
  with members:
    - "distributed inference in tree networks using coding theory"

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.IT(Information Theory)