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: "geographic gossip efficient averaging for sensor networks"
  Abstract: Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste of energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of n and radicn, respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy e using O((n1.5radiclogn) logisin-1) radio transmissions, which yields a radicn/ log n factor improvement over standard gossip algorithms. We illustrate these theoretical results with experimental comparisons between our algorithm and standard methods as applied to various classes of random fields.

Connected through hyperedge HE-1 (induced by source paper "quantifying differential privacy of gossip protocols in general networks"):
  with members:
    - "who started this rumor quantifying the natural differential privacy guarantees of gossip protocols"

Connected through hyperedge HE-2 (induced by source paper "a new perspective on randomized gossip algorithms"):
  with members:
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "gossip algorithms for distributed signal processing"
    - "order optimal consensus through randomized path averaging"

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"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-4 (induced by source paper "learning based distributed detection estimation in sensor networks with unknown sensor defects"):
  with members:
    - "switching to learn"
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "distributed detection over noisy networks large deviations analysis"
    - "distributed recursive least squares stability and performance analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "a chemistry inspired framework for achieving consensus in wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "toward resource optimal consensus over the wireless medium"):
  with members:
    - "convergence speed of the consensus algorithm with interference and sparse long range connectivity"
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "multiscale gossip for efficient decentralized averaging in wireless packet networks"
    - "order optimal consensus through randomized path averaging"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-7 (induced by source paper "analysis of sum weight like algorithms for averaging in wireless sensor networks"):
  with members:
    - "greedy gossip with eavesdropping"
    - "gossip algorithms for distributed signal processing"
    - "order optimal consensus through randomized path averaging"

Connected through hyperedge HE-8 (induced by source paper "belief consensus algorithms for fast distributed target tracking in wireless sensor networks"):
  with members:
    - "loopy belief propagation as a basis for communication in sensor networks"
    - "gossip algorithms for distributed signal processing"

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)