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: "higher dimensional consensus learning in large scale networks"
  Abstract: The paper considers higher dimensional consensus (HDC). HDC is a general class of linear distributed algorithms for large-scale networks that generalizes average-consensus and includes other interesting distributed algorithms, like sensor localization, leader-follower algorithms in multiagent systems, or distributed Jacobi algorithm. In HDC, the network nodes are partitioned into ?anchors?, nodes whose states are fixed over the HDC iterations, and ?sensors?, nodes whose states are updated by the algorithm. The paper starts by briefly considering what we call the forward problem by presenting the conditions for HDC to converge, the limiting state to which it converges, and what is its convergence rate. The main focus of the paper is the inverse or design problem, i.e., learning the weights or parameters of the HDC so that the algorithm converges to a desired prespecified state. This generalizes the well-known problem of designing the weights in average-consensus. We pose learning as a constrained nonconvex optimization problem that we cast in the framework of multiobjective optimization (MOP) and to which we apply Pareto optimality. We derive the solution to the learning problem by proving relevant properties satisfied by the MOP solutions and by the Pareto front. Finally, the paper shows how the MOP approach leads to interesting tradeoffs (speed of convergence versus performance) arising in resource constrained networks. Simulation studies illustrate our approach for a leader-follower architecture in multiagent systems.

Connected through hyperedge HE-1 (induced by source paper "cooperative network synchronization asymptotic analysis"):
  with members:
    - "on the outage probability of localization in randomly deployed wireless networks"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "cooperative joint localization and clock synchronization based on gaussian message passing in asynchronous wireless networks"
    - "performance limits and geometric properties of array localization"
    - "distributed clock skew and offset estimation in wireless sensor networks asynchronous algorithm and convergence analysis"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"

Connected through hyperedge HE-2 (induced by source paper "localization in internets of mobile agents a linear approach"):
  with members:
    - "an opportunistic linear convex algorithm for localization in mobile robot networks"
    - "localization in mobile networks via virtual convex hulls"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"
    - "fundamental limits of wideband localization part i a general framework"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-3 (induced by source paper "belief control strategies for interactions over weak graphs"):
  with members:
    - "active sensing of social networks"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "information diffusion in social sensing"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"

Connected through hyperedge HE-4 (induced by source paper "active sensing of social networks"):
  with members:
    - "signal processing on graphs modeling causal relations in big data"
    - "the social system identification problem"
    - "analysis and control of beliefs in social networks"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"

Connected through hyperedge HE-5 (induced by source paper "fictitious play with inertia learns pure equilibria in distributed games with incomplete information"):
  with members:
    - "diffusion adaptation over networks"
    - "on the structure of weakly acyclic games"
    - "learning equilibria with partial information in decentralized wireless networks"
    - "gossip algorithms for distributed signal processing"
    - "methodologies for analyzing equilibria in wireless games"

Connected through hyperedge HE-6 (induced by source paper "social learning over weakly connected graphs"):
  with members:
    - "data falsification attacks on consensus based detection systems"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "interactive sensing and decision making in social networks"

Connected through hyperedge HE-7 (induced by source paper "information exchange and learning dynamics over weakly connected adaptive networks"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "multitask diffusion adaptation over networks"
    - "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"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "distributed strongly convex optimization"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-8 (induced by source paper "distributed decision making over adaptive networks"):
  with members:
    - "on the influence of informed agents on learning and adaptation over networks"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

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)