human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distributed detection with empirically observed statistics"):
  Abstract: Consider a distributed detection problem in which the underlying distributions of the observations are unknown; instead of these distributions, noisy versions of empirically observed statistics are available to the fusion center. These empirically observed statistics, together with source (test) sequences, are transmitted through different channels to the fusion center. The fusion center decides which distribution the source sequence is sampled from based on these data. For the binary case, we derive the optimal type-II error exponent given that the type-I error decays exponentially fast. The type-II error exponent is maximized over the proportions of channels for both source and training sequences. We conclude that as the ratio of the lengths of training to test sequences $\alpha$ tends to infinity, using only one channel is optimal. By calculating the derived exponents numerically, we conjecture that the same is true when $\alpha$ is finite. We relate our results to the classical distributed detection problem studied by Tsitsiklis, in which the underlying distributions are known. Finally, our results are extended to the case of $m$-ary distributed detection with a rejection option.

Includes members:
  Member "second order asymptotically optimal statistical classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")
  Member "anonymous heterogeneous distributed detection optimal decision rules error exponents and the price of anonymity":
    also connected through other hyperedges:
      (none)
  Member "distributed hypothesis testing with privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy aware distributed hypothesis testing")
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "asymptotically optimal matching of multiple sequences to source distributions and training sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")
  Member "asymmetric evaluations of erasure and undetected error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel")
  Member "privacy against statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "data fusion trees for detection does architecture matter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive privacy for a linear dynamical system")

Question: Please tell me which class the source paper 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)