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: "topology for distributed inference on graphs"
  Abstract: Let $N$ local decision makers in a sensor network communicate with their neighbors to reach a decision \emph{consensus}. Communication is local, among neighboring sensors only, through noiseless or noisy links. We study the design of the network topology that optimizes the rate of convergence of the iterative decision consensus algorithm. We reformulate the topology design problem as a spectral graph design problem, namely, maximizing the eigenratio~$\gamma$ of two eigenvalues of the graph Laplacian~$L$, a matrix that is naturally associated with the interconnectivity pattern of the network. This reformulation avoids costly Monte Carlo simulations and leads to the class of non-bipartite Ramanujan graphs for which we find a lower bound on~$\gamma$. For Ramanujan topologies and noiseless links, the local probability of error converges much faster to the overall global probability of error than for structured graphs, random graphs, or graphs exhibiting small-world characteristics. With noisy links, we determine the optimal number of iterations before calling a decision. Finally, we introduce a new class of random graphs that are easy to construct, can be designed with arbitrary number of sensors, and whose spectral and convergence properties make them practically equivalent to Ramanujan topologies.

Connected through hyperedge HE-1 (induced by source paper "distributed detection in ad hoc networks through quantized consensus"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over noisy networks large deviations analysis"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-2 (induced by source paper "byzantine resilient locally optimum detection using collaborative autonomous networks"):
  with members:
    - "data falsification attacks on consensus based detection systems"

Connected through hyperedge HE-3 (induced by source paper "optimizing the gossip algorithm with non uniform clock distribution over classical quantum networks"):
  with members:
    - "finite time convergent gossiping"
    - "optimizing the convergence rate of the continuous time quantum consensus"
    - "optimizing the convergence rate of the quantum consensus a discrete time model"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "the impact of mobility on gossip algorithms"
    - "geographic gossip efficient averaging for sensor networks"

Connected through hyperedge HE-4 (induced by source paper "convergence analysis for regular wireless consensus networks"):
  with members:
    - "sensor networks with random links topology design for distributed consensus"

Connected through hyperedge HE-5 (induced by source paper "bayesian discovery of threat networks"):
  with members:
    - "discrete signal processing on graphs frequency analysis"
    - "graph spectra and the detectability of community structure in networks"
    - "quick detection of nodes with large degrees"
    - "empirical comparison of algorithms for network community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-6 (induced by source paper "network detection theory and performance"):
  with members:
    - "graph spectra and the detectability of community structure in networks"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-7 (induced by source paper "large deviations performance of consensus innovations distributed detection with non gaussian observations"):
  with members:
    - "distributed detection over noisy networks large deviations analysis"
    - "optimal precoding design and power allocation for decentralized detection of deterministic signals"
    - "sensor selection for event detection in wireless sensor networks"
    - "distributed detection over time varying networks large deviations analysis"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-8 (induced by source paper "distributed detection over noisy networks large deviations analysis"):
  with members:
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed detection over time varying networks large deviations analysis"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "sensor networks with random links topology design for distributed consensus"

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)