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: "mechanism design for resource allocation in networks with intergroup competition and intragroup sharing"
  Abstract: We consider a network where strategic agents, who are contesting for allocation of resources, are divided into fixed groups. The network control protocol is such that within each group agents get to share the resource and across groups they contest for it. A prototypical example is the allocation of data rate on a network with multicast/multirate architecture. Compared to the unicast architecture (which is a special case), the multicast/multirate architecture can result in substantial bandwidth savings. However, design of a market mechanism in such a scenario requires dealing with both private and public good problems as opposed to just private goods in unicast. #R##N#The mechanism proposed in this work ensures that social welfare maximizing allocation on such a network is realized at all Nash equilibria (NE) i.e., full implementation in NE. In addition it is individually rational, i.e., agents have an incentive to participate in the mechanism. The mechanism, which is constructed in a quasi-systematic way starting from the dual of the centralized problem, has a number of useful properties. Specifically, due to a novel allocation scheme, namely "radial projection", the proposed mechanism results in feasible allocation even off equilibrium. This is a practical necessity for any realistic mechanism since agents have to "learn" the NE through a dynamic process. Finally, it is shown how strong budget balance at equilibrium can be achieved with a minimal increase in message space as an add-on to a weakly budget balanced mechanism.

Connected through hyperedge HE-1 (induced by source paper "distributed mechanism design for multicast transmission"):
  with members:
    - "distributed mechanism design with learning guarantees"
    - "differentially private distributed optimization"

Connected through hyperedge HE-2 (induced by source paper "distributed mechanism design with learning guarantees"):
  with members:
    - "distributed nonconvex multiagent optimization over time varying 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.GT(Computer Science and Game Theory)