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: "distributed lossy averaging"
  Abstract: An information theoretic formulation of the distributed averaging problem previously studied in computer science and control is presented. We assume a network with m nodes each observing a WGN source. The nodes communicate and perform local processing with the goal of computing the average of the sources to within a prescribed mean squared error distortion. The network rate distortion function R^*(D) for a 2-node network with correlated Gaussian sources is established. A general cutset lower bound on R^*(D) is established and shown to be achievable to within a factor of 2 via a centralized protocol over a star network. A lower bound on the network rate distortion function for distributed weighted-sum protocols, which is larger in order than the cutset bound by a factor of log m is established. An upper bound on the network rate distortion function for gossip-base weighted-sum protocols, which is only log log m larger in order than the lower bound for a complete graph network, is established. The results suggest that using distributed protocols results in a factor of log m increase in order relative to centralized protocols.

Connected through hyperedge HE-1 (induced by source paper "on the interactive communication cost of the distributed nearest lattice point problem"):
  with members:
    - "communication cost of transforming a nearest plane partition to the voronoi partition"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-2 (induced by source paper "generalized geometric programming for rate allocation in consensus"):
  with members:
    - "an overview of multi processor approximate message passing"
    - "optimal trade offs in multi processor approximate message passing"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "performance trade offs in multi processor approximate message passing"
    - "toward resource optimal consensus over the wireless medium"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"
    - "gossip algorithms for distributed signal processing"
    - "network based consensus averaging with general noisy channels"

Connected through hyperedge HE-3 (induced by source paper "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"):
  with members:
    - "geographic gossip efficient averaging for sensor networks"
    - "network based consensus averaging with general noisy channels"

Connected through hyperedge HE-4 (induced by source paper "gossip algorithms for distributed signal processing"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "optimization and analysis of distributed averaging with short node memory"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "the impact of mobility on gossip algorithms"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributed and recursive parameter estimation in parametrized linear state space models"

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)