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: "robust decentralized detection and social learning in tandem networks"
  Abstract: We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class defined by a 2-alternating capacity. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.

Connected through hyperedge HE-1 (induced by source paper "an unsupervised bayesian neural network for truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"
    - "generalized denoising auto encoders as generative models"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-2 (induced by source paper "non bayesian social learning with imperfect private signal structure"):
  with members:
    - "indian buffet game with negative network externality and non bayesian social learning"

Connected through hyperedge HE-3 (induced by source paper "compressive privacy for a linear dynamical system"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "information theoretic attacks in the smart grid"
    - "privacy aware mmse estimation"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "differential privacy as a mutual information constraint"
    - "distributed local linear parameter estimation using gaussian spawn"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"

Connected through hyperedge HE-4 (induced by source paper "decentralized detection with robust information privacy protection"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "multilayer nonlinear processing for information privacy in sensor networks"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "big data privacy in the internet of things era"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-5 (induced by source paper "on the relationship between inference and data privacy in decentralized iot networks"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "learning in hierarchical social networks"
    - "differentially private iterative synchronous consensus"
    - "privacy against statistical inference"
    - "the value of feedback in decentralized detection"

Connected through hyperedge HE-6 (induced by source paper "using social network information in bayesian truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-7 (induced by source paper "network topology inference using information cascades with limited statistical knowledge"):
  with members:
    - "estimating infection sources in networks using partial timestamps"
    - "back to the source an online approach for sensor placement and source localization"
    - "network topology inference from spectral templates"
    - "infection spreading and source identification a hide and seek game"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "learning laplacian matrix in smooth graph signal representations"
    - "how to identify an infection source with limited observations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation"):
  with members:
    - "distributed local linear parameter estimation using gaussian spawn"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "distributed localization of a rf target in nlos environments"
    - "multitask diffusion adaptation over asynchronous networks"
    - "diffusion lms for clustered multitask networks"

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)