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: "adaptive spatial aloha fairness and stochastic geometry"
  Abstract: This work aims at combining adaptive protocol design, utility maximization and stochastic geometry. We focus on a spatial adaptation of Aloha within the framework of ad hoc networks. We consider quasi-static networks in which mobiles learn the local topology and incorporate this information to adapt their medium access probability (MAP) selection to their local environment. We consider the cases where nodes cooperate in a distributed way to maximize the global throughput or to achieve either proportional fair or max-min fair medium access. In the proportional fair case, we show that nodes can compute their optimal MAPs as solutions to certain fixed point equations. In the maximum throughput case, the optimal MAPs are obtained through a Gibbs Sampling based algorithm. In the max min case, these are obtained as the solution of a convex optimization problem. The main performance analysis result of the paper is that this type of distributed adaptation can be analyzed using stochastic geometry in the proportional fair case. In this case, we show that, when the nodes form a homogeneous Poisson point process in the Euclidean plane, the distribution of the optimal MAP can be obtained from that of a certain shot noise process w.r.t. the node Poisson point process and that the mean utility can also be derived from this distribution. We discuss the difficulties to be faced for analyzing the performance of the other cases (maximum throughput and max-min fairness). Numerical results illustrate our findings and quantify the gains brought by spatial adaptation in such networks.

Connected through hyperedge HE-1 (induced by source paper "adaptive csma under the sinr model efficient approximation algorithms for throughput and utility maximization"):
  with members:
    - "spatial csma a distributed scheduling algorithm for the sir model with time varying channels"
    - "how well can graphs represent wireless interference"
    - "exploiting the past to reduce delay in csma scheduling a high order markov chain approach"
    - "loopy belief propagation for approximate inference an empirical study"
    - "applications of belief propagation in csma wireless networks"
    - "fast mixing of parallel glauber dynamics and low delay csma scheduling"
    - "distributed random access algorithm scheduling and congesion control"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-2 (induced by source paper "maximum sum rate of slotted aloha with capture"):
  with members:
    - "coded random access applying codes on graphs to design random access protocols"
    - "managing interference correlation through random medium access"
    - "aloha random access that operates as a rateless code"
    - "random access an information theoretic perspective"
    - "random access with physical layer network coding"
    - "an overview of the transmission capacity of wireless networks"
    - "spatial and temporal correlation of the interference in aloha ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "spatial csma a distributed scheduling algorithm for the sir model with time varying channels"):
  with members:
    - "csma over time varying channels optimality uniqueness and limited backoff rate"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "on spatial capacity of wireless ad hoc networks with threshold based scheduling"):
  with members:
    - "spatial throughput characterization in cognitive radio networks with threshold based opportunistic spectrum access"
    - "exploiting regional differences a spatially adaptive random access"
    - "analytical modeling of uplink cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"
    - "an overview of the transmission capacity of wireless networks"
    - "on a game theoretic approach to capacity maximization in wireless networks"
    - "fractional power control for decentralized wireless networks"
    - "the effect of fading channel inversion and threshold scheduling on ad hoc networks"

Connected through hyperedge HE-5 (induced by source paper "exploiting regional differences a spatially adaptive random access"):
  with members:
    - "an iterative algorithm for optimal carrier sensing threshold in random csma ca wireless networks"
    - "an overview of the transmission capacity of wireless networks"
    - "the effect of fading channel inversion and threshold scheduling on ad hoc networks"

Connected through hyperedge HE-6 (induced by source paper "analysis of a proportionally fair and locally adaptive spatial aloha in poisson networks"):
  with members:
    - "fractional power control for decentralized wireless 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.NI(Networking and Internet Architecture)