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: "dynamic network formation with incomplete information"
  Abstract: How do networks form and what is their ultimate topology? Most of the literature that addresses these questions assumes complete information: agents know in advance the value of linking to other agents, even with agents they have never met and with whom they have had no previous interaction (direct or indirect). This paper addresses the same questions under what seems to us to be the much more natural assumption of incomplete information: agents do not know in advance -- but must learn -- the value of linking to agents they have never met. We show that the assumption of incomplete information has profound implications for the process of network formation and the topology of networks that ultimately form. Under complete information, the networks that form and are stable typically have a star, wheel or core-periphery form, with high-value agents in the core. Under incomplete information, the presence of positive externalities (the value of indirect links) implies that a much wider collection of network topologies can emerge and be stable. Moreover, even when the topologies that emerge are the same, the locations of agents can be very different. For instance, when information is incomplete, it is possible for a hub-and-spokes network with a low-value agent in the center to form and endure permanently: an agent can achieve a central position purely as the result of chance rather than as the result of merit. Perhaps even more strikingly: when information is incomplete, a connected network could form and persist even if, when information were complete, no links would ever form, so that the final form would be a totally disconnected network. All of this can occur even in settings where agents eventually learn everything so that information, although initially incomplete, eventually becomes complete.

Connected through hyperedge HE-1 (induced by source paper "hierarchical network formation games"):
  with members:
    - "polarization and fluctuations in signed social networks"
    - "dynamic network formation with foresighted agents"

Connected through hyperedge HE-2 (induced by source paper "distributed topology design for network coding deployed large scale sensor networks"):
  with members:
    - "network coding based evolutionary network formation for dynamic wireless networks"
    - "cost efficient qos aware data acquisition point placement for advanced metering infrastructure"
    - "decoding delay minimization in inter session network coding"
    - "distributed rate allocation in inter session network coding"
    - "mac centered cooperation synergistic design of network coding multi packet reception and improved fairness to increase network throughput"
    - "relay selection with network coding in two way relay channels"
    - "sensor networks with random links topology design for distributed consensus"

Connected through hyperedge HE-3 (induced by source paper "self organizing networks of information gathering cognitive agents"):
  with members:
    - "social data offloading in d2d enhanced cellular networks by network formation games"
    - "conjoining speeds up information diffusion in overlaying social physical networks"
    - "a game theoretic analysis of incentives in content production and sharing over peer to peer networks"
    - "coalition formation games for distributed cooperation among roadside units in vehicular networks"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "minimum cost distributed source coding over a network"
    - "network information flow with correlated sources"

Connected through hyperedge HE-4 (induced by source paper "a micro foundation of social capital in evolving social networks"):
  with members:
    - "a model for social network formation efficiency stability and dynamics"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "a micro foundational model and analysis of evolving social networks"):
  with members:
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "supervised random walks predicting and recommending links in social networks"

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.SI(Social and Information Networks)