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: "whose opinion to follow in multihypothesis social learning a large deviations perspective"
  Abstract: We consider a multihypothesis social learning problem in which an agent has access to a set of private observations and chooses an opinion from a set of experts to incorporate into its final decision. To model individual biases, we allow the agent and experts to have general loss functions and possibly different decision spaces. We characterize the loss exponents of both the agent and experts, and provide an asymptotically optimal method for the agent to choose the best expert to follow. We show that up to asymptotic equivalence, the worst loss exponent for the agent is achieved when it adopts the 0-1 loss function, which assigns a loss of 0 if the true hypothesis is declared and a loss of 1 otherwise. We introduce the concept of hypothesis-loss neutrality, and show that if the agent adopts a particular policy that is hypothesis-loss neutral, then it ignores all experts whose decision spaces are smaller than its own. On the other hand, if experts have the same decision space as the agent, then choosing an expert with the same loss function as itself is not necessarily optimal for the agent, which is somewhat counter-intuitive. We derive sufficient conditions for when it is optimal for the agent with 0-1 loss function to choose an expert with the same loss function.

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"
    - "robust decentralized detection and social learning in tandem networks"
    - "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 "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"
    - "robust decentralized detection and social learning in tandem networks"

Connected through hyperedge HE-3 (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"
    - "robust decentralized detection and social learning in tandem networks"
    - "big data privacy in the internet of things era"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-4 (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"
    - "robust decentralized detection and social learning in tandem networks"
    - "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-5 (induced by source paper "using social network information in bayesian truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "robust decentralized detection and social learning in tandem networks"
    - "a survey on truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-6 (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"
    - "robust decentralized detection and social learning in tandem networks"
    - "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-7 (induced by source paper "multilayer nonlinear processing for information privacy in sensor networks"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "deep residual learning for image recognition"
    - "geo indistinguishability differential privacy for location based systems"
    - "privacy against statistical inference"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-8 (induced by source paper "infection spreading and source identification a hide and seek game"):
  with members:
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "robust decentralized detection and social learning in tandem networks"
    - "how to identify an infection source with limited observations"
    - "spy vs spy rumor source obfuscation"
    - "graphical evolutionary game for information diffusion over social networks"
    - "evolutionary dynamics of information diffusion over social networks"
    - "identifying infection sources and regions in large networks"
    - "rooting out the rumor culprit from suspects"

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)