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 maximum likelihood sensor network localization"
  Abstract: We propose a class of convex relaxations to solve the sensor network localization problem, based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the relaxations, depends on the noise probability density function (PDF) of the collected measurements. We derive a computational efficient edge-based version of this ML convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge-based convex programs locally by communicating only with their close neighbors. This algorithm relies on the alternating direction method of multipliers (ADMM), it converges to the centralized solution, it can run asynchronously, and it is computation error-resilient. Finally, we compare our proposed distributed scheme with other available methods, both analytically and numerically, and we argue the added value of ADMM, especially for large-scale networks.

Connected through hyperedge HE-1 (induced by source paper "peregrine network localization and navigation with scalable inference and efficient operation"):
  with members:
    - "pricing mechanisms for crowd sensed spatial statistics based radio mapping"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"
    - "cramer rao bounds for joint rss doa based primary user localization in cognitive radio networks"
    - "sigma point belief propagation"
    - "fundamental limits of wideband localization part ii cooperative networks"
    - "fundamental limits of wideband localization part i a general framework"

Connected through hyperedge HE-2 (induced by source paper "accelerating admm for efficient simulation and optimization"):
  with members:
    - "parallel and scalable heat methods for geodesic distance computation"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "on uniquely registrable networks"):
  with members:
    - "large scale sensor network localization via rigid subnetwork registration"
    - "global registration of multiple point clouds using semidefinite programming"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "on affine rigidity"

Connected through hyperedge HE-4 (induced by source paper "time varying constrained proximal type dynamics in multi agent network games"):
  with members:
    - "an asynchronous forward backward distributed generalized nash equilibrium seeking algorithm"
    - "game theoretic analysis of the hegselmann krause model for opinion dynamics in finite dimensions"

Connected through hyperedge HE-5 (induced by source paper "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"):
  with members:
    - "semanticpaint interactive 3d labeling and learning at your fingertips"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "real time monocular object slam"

Connected through hyperedge HE-6 (induced by source paper "locdyn robust distributed localization for mobile underwater networks"):
  with members:
    - "a spectral learning approach to range only slam"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"

Connected through hyperedge HE-7 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "parallel selective algorithms for big data optimization"
    - "distributed detection and estimation in wireless sensor networks"
    - "distributed compressed sensing for static and time varying networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "sparse distributed learning based on diffusion adaptation"

Connected through hyperedge HE-8 (induced by source paper "cooperative localization in wsns a hybrid convex non convex solution"):
  with members:
    - "explicit convergence rate of a distributed alternating direction method of multipliers"

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)