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: "detection of gauss markov random fields with nearest neighbor dependency"
  Abstract: The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) is analyzed. Assuming an acyclic dependency graph, an expression for the log-likelihood ratio of detection is derived. Assuming random placement of nodes over a large region according to the Poisson or uniform distribution and nearest-neighbor dependency graph, the error exponent of the Neyman-Pearson detector is derived using large-deviations theory. The error exponent is expressed as a dependency-graph functional and the limit is evaluated through a special law of large numbers for stabilizing graph functionals. The exponent is analyzed for different values of the variance ratio and correlation. It is found that a more correlated GMRF has a higher exponent at low values of the variance ratio whereas the situation is reversed at high values of the variance ratio.

Connected through hyperedge HE-1 (induced by source paper "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "generalized thresholding and online sparsity aware learning in a union of subspaces"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-2 (induced by source paper "distributed detection and estimation in wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "energy scaling laws for distributed inference in random fusion networks"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems"
    - "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels"

Connected through hyperedge HE-3 (induced by source paper "balancing lifetime and classification accuracy of wireless sensor networks"):
  with members:
    - "comparison of csma based mac protocols of wireless sensor networks"
    - "on the stability of flow aware csma"

Connected through hyperedge HE-4 (induced by source paper "energy scaling laws for distributed inference in random fusion networks"):
  with members:
    - "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis"
    - "sensor configuration and activation for field detection in large sensor arrays"

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)