human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the influence of informed agents on learning and adaptation over networks"):
  Abstract: Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of agents: informed agents and uninformed agents. The former receive new data regularly and perform consultation and in-network processing, while the latter do not collect data and only participate in the consultation tasks. We examine the performance of LMS diffusion strategies for distributed estimation over networks as a function of the proportion of informed agents and their distribution in space. The results reveal some interesting trade-offs between convergence rate and mean-square performance. In particular, among other results, it is shown that the mean-square performance of adaptive networks does not necessarily improve with a larger proportion of informed agents. Instead, it is established that if the set of informed agents is enlarged, the convergence rate of the network becomes faster albeit at the expense of some deterioration in mean-square performance. The results further establish that uninformed agents play an important role in determining the steady-state performance of the network and that it is preferable to keep some of the highly noisy or highly connected agents uninformed. The arguments reveal an important interplay among three factors: the number and distribution of informed agents in the network, the convergence rate of the learning process, and the estimation accuracy in steady-state. Expressions that quantify these relations are derived, and simulations are included to support the theoretical findings. We illustrate application of the results to two network models, namely, the Erdos-Renyi and scale-free models.

Includes members:
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "performance limits for distributed estimation over lms adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detection under one bit messaging over adaptive networks")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised learning under distributed features")
  Member "higher dimensional consensus learning in large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative network synchronization asymptotic analysis")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed learning in wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diffusion lms over multitask networks")

Question: Please tell me which class the source paper 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)