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: "data fusion trees for detection does architecture matter"
  Abstract: We consider the problem of decentralized detection in a network consisting of a large number of nodes arranged as a tree of bounded height, under the assumption of conditionally independent, identically distributed observations. We characterize the optimal error exponent under a Neyman-Pearson formulation. We show that the Type II error probability decays exponentially fast with the number of nodes, and the optimal error exponent is often the same as that corresponding to a parallel configuration. We provide sufficient, as well as necessary, conditions for this to happen. For those networks satisfying the sufficient conditions, we propose a simple strategy that nearly achieves the optimal error exponent, and in which all non-leaf nodes need only send 1-bit messages.

Connected through hyperedge HE-1 (induced by source paper "distributed detection with empirically observed statistics"):
  with members:
    - "second order asymptotically optimal statistical classification"
    - "anonymous heterogeneous distributed detection optimal decision rules error exponents and the price of anonymity"
    - "distributed hypothesis testing with privacy constraints"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "privacy against statistical inference"

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"
    - "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-4 (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"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "geo indistinguishability differential privacy for location based systems"
    - "privacy against statistical inference"

Connected through hyperedge HE-5 (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"
    - "the value of feedback in decentralized detection"

Connected through hyperedge HE-6 (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"
    - "the value of feedback in decentralized detection"
    - "error probability bounds for balanced binary relay trees"

Connected through hyperedge HE-7 (induced by source paper "distributed inference in tree networks using coding theory"):
  with members:
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "detection performance in balanced binary relay trees with node and link failures"

Connected through hyperedge HE-8 (induced by source paper "a general method for the design of tree networks under communication constraints"):
  with members:
    - "bayesian design of tandem networks for distributed detection with multi bit sensor decisions"

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)