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: "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
  Abstract: In this paper, we develop a social group utility maximization (SGUM) framework for cooperative wireless networking that takes into account both social relationships and physical coupling among users. We show that this framework provides rich modeling flexibility and spans the continuum between non-cooperative game and network utility maximization (NUM) -- two traditionally disjoint paradigms for network optimization. Based on this framework, we study three important applications of SGUM, in database assisted spectrum access, power control, and random access control, respectively. For the case of database assisted spectrum access, we show that the SGUM game is a potential game and always admits a socially-aware Nash equilibrium (SNE). We develop a randomized distributed spectrum access algorithm that can asymptotically converge to the optimal SNE, derive upper bounds on the convergence time, and also quantify the trade-off between the performance and convergence time of the algorithm. We further show that the performance gap of SNE by the algorithm from the NUM solution decreases as the strength of social ties among users increases and the performance gap is zero when the strengths of social ties among users reach the maximum values. For the cases of power control and random access control, we show that there exists a unique SNE. Furthermore, as the strength of social ties increases from the minimum to the maximum, a player's SNE strategy migrates from the Nash equilibrium strategy in a standard non-cooperative game to the socially-optimal strategy in network utility maximization. Furthermore, we show that the SGUM framework can be generalized to take into account both positive and negative social ties among users and can be a useful tool for studying network security problems.

Connected through hyperedge HE-1 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "deep reinforcement learning for intelligent transportation systems"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dynamic pricing for revenue maximization in mobile social data market with network effects"):
  with members:
    - "mobile data trading behavioral economics analysis and algorithm design"
    - "a socially aware incentive mechanism for mobile crowdsensing service market"
    - "competition and cooperation analysis for data sponsored market a network effects model"
    - "price discrimination for energy trading in smart grid a game theoretic approach"
    - "time and location aware mobile data pricing"
    - "a survey of smart data pricing past proposals current plans and future trends"
    - "optimal pricing in networks with externalities"
    - "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach"

Connected through hyperedge HE-3 (induced by source paper "amazon in the white space social recommendation aided distributed spectrum access"):
  with members:
    - "imitation based social spectrum sharing"
    - "spatial spectrum access game"
    - "smart streaming for online video services"
    - "adaptive channel recommendation for opportunistic spectrum access"
    - "cognitive medium access exploration exploitation and competition"

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.NI(Networking and Internet Architecture)