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 adaptive networks a graphical evolutionary game theoretic view"
  Abstract: Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.

Connected through hyperedge HE-1 (induced by source paper "a graphical evolutionary game approach to social learning"):
  with members:
    - "bayesian quadratic network game filters"
    - "graphical evolutionary game for information diffusion over social networks"
    - "evolutionary dynamics of information diffusion over social networks"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "on learning with finite memory"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "game theory for signal processing in networks"):
  with members:
    - "understanding game theory via wireless power control"
    - "crawford sobel meet lloyd max on the grid"
    - "coalitional games in miso interference channels epsilon core and coalition structure stable set"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"
    - "distributed cooperative sensing in cognitive radio networks an overlapping coalition formation approach"
    - "jamming games in the mimo wiretap channel with an active eavesdropper"
    - "distributed learning policies for power allocation in multiple access channels"
    - "on the nash equilibria in decentralized parallel interference channels"

Connected through hyperedge HE-3 (induced by source paper "graphical evolutionary game for information diffusion over social networks"):
  with members:
    - "conjoining speeds up information diffusion in overlaying social physical networks"
    - "the role of social networks in information diffusion"
    - "a game theoretical approach to broadcast information diffusion in social networks"

Connected through hyperedge HE-4 (induced by source paper "evolutionary dynamics of information diffusion over social networks"):
  with members:
    - "graphical evolutionary game for information diffusion over social networks"
    - "conjoining speeds up information diffusion in overlaying social physical networks"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-5 (induced by source paper "multitask diffusion adaptation over networks"):
  with members:
    - "distributed decision making over adaptive networks"
    - "diffusion lms for clustered multitask networks"
    - "distributed pareto optimization via diffusion strategies"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"

Connected through hyperedge HE-6 (induced by source paper "a novel analytical method for evolutionary graph theory problems"):
  with members:
    - "approximating fixation probabilities in the generalized moran process"

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.GT(Computer Science and Game Theory)