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: "performance limits for distributed estimation over lms adaptive networks"
  Abstract: In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks. The results highlight some useful properties for distributed adaptation in comparison to fusion-based centralized solutions. The analysis establishes that, by optimizing over the combination weights, diffusion strategies can deliver lower excess-mean-square-error than centralized solutions employing traditional block or incremental LMS strategies. We first study in some detail the situation involving combinations of two adaptive agents and then extend the results to generic N -node ad-hoc networks. In the latter case, we establish that, for sufficiently small step-sizes, diffusion strategies can outperform centralized block or incremental LMS strategies by optimizing over left-stochastic combination weighting matrices. The results suggest more efficient ways for organizing and processing data at fusion centers, and present useful adaptive strategies that are able to enhance performance when implemented in a distributed manner.

Connected through hyperedge HE-1 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-2 (induced by source paper "diff dac distributed actor critic for multitask deep reinforcement learning"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"
    - "connecting generative adversarial networks and actor critic methods"
    - "asynchronous methods for deep reinforcement learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation"):
  with members:
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "distributed localization of a rf target in nlos environments"
    - "multitask diffusion adaptation over asynchronous networks"

Connected through hyperedge HE-4 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-5 (induced by source paper "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"):
  with members:
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"

Connected through hyperedge HE-6 (induced by source paper "diffusion lms strategies in sensor networks with noisy input data"):
  with members:
    - "distributed decision making over adaptive networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-7 (induced by source paper "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "distributed decision making over adaptive networks"
    - "a novel family of adaptive filtering algorithms based on the logarithmic cost"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"

Connected through hyperedge HE-8 (induced by source paper "distributed estimation for adaptive networks based on serial inspired diffusion"):
  with members:
    - "adaptive link selection strategies for distributed estimation in wireless sensor networks"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "a sparsity promoting adaptive algorithm for distributed learning"

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)