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: "the value of feedback in decentralized detection"
  Abstract: We consider the decentralized binary hypothesis testing problem in networks with feedback, where some or all of the sensors have access to compressed summaries of other sensors' observations. We study certain two-message feedback architectures, in which every sensor sends two messages to a fusion center, with the second message based on full or partial knowledge of the first messages of the other sensors. We also study one-message feedback architectures, in which each sensor sends one message to a fusion center, with a group of sensors having full or partial knowledge of the messages from the sensors not in that group. Under either a Neyman-Pearson or a Bayesian formulation, we show that the asymptotically optimal (in the limit of a large number of sensors) detection performance (as quantified by error exponents) does not benefit from the feedback messages, if the fusion center remembers all sensor messages. However, feedback can improve the Bayesian detection performance in the one-message feedback architecture if the fusion center has limited memory; for that case, we determine the corresponding optimal error exponents.

Connected through hyperedge HE-1 (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"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "big data privacy in the internet of things era"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "towards information privacy for the internet of things"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "privacy in the internet of things threats and challenges"
    - "distributed localization of a rf target in nlos environments"
    - "geo indistinguishability differential privacy for location based systems"
    - "privacy against statistical inference"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "whose opinion to follow in multihypothesis social learning a large deviations perspective"):
  with members:
    - "identifying infection sources and regions in large networks"
    - "controlled sensing for multihypothesis testing"
    - "on learning with finite memory"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

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)