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: "chernoff information of exponential families"
  Abstract: Chernoff information upper bounds the probability of error of the optimal Bayesian decision rule for $2$-class classification problems. However, it turns out that in practice the Chernoff bound is hard to calculate or even approximate. In statistics, many usual distributions, such as Gaussians, Poissons or frequency histograms called multinomials, can be handled in the unified framework of exponential families. In this note, we prove that the Chernoff information for members of the same exponential family can be either derived analytically in closed form, or efficiently approximated using a simple geodesic bisection optimization technique based on an exact geometric characterization of the "Chernoff point" on the underlying statistical manifold.

Connected through hyperedge HE-1 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  with members:
    - "start making sense semantic plane filtering and control for post 5g connectivity"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
    - "deep joint source channel coding for wireless image transmission"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a brief introduction to machine learning for engineers"

Connected through hyperedge HE-2 (induced by source paper "second order statistics analysis and comparison between arithmetic and geometric average fusion application to multi sensor target tracking"):
  with members:
    - "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion"
    - "distributed smc phd fusion for partial arithmetic average consensus"
    - "a distributed particle phd filter with arithmetic average phd fusion"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-3 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions"):
  with members:
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mixture entropy with pairwise distances"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "multivariate f divergence estimation with confidence"
    - "k mle a fast algorithm for learning statistical mixture models"
    - "on renyi and tsallis entropies and divergences for exponential families"
    - "the burbea rao and bhattacharyya centroids"
    - "generalised pinsker inequalities"

Connected through hyperedge HE-4 (induced by source paper "compressed hypothesis testing to mix or not to mix"):
  with members:
    - "quickest search over multiple sequences with mixed observations"
    - "on the limits of sequential testing in high dimensions"
    - "kalman filtered compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)