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: "explicit back off rates for achieving target throughputs in csma ca networks"
  Abstract: Carrier-sense multiple access/collision avoidance networks have often been analyzed using a stylized model that is fully characterized by a vector of back-off rates and a conflict graph. Furthermore, for any achievable throughput vector    $\vec \theta $   , the existence of a unique vector    $\vec \nu (\vec \theta )$    of back-off rates that achieves this throughput vector was proven. Although this unique vector can in principle be computed iteratively, the required time complexity grows exponentially in the network size, making this only feasible for small networks. In this paper, we present an explicit formula for the unique vector of back-off rates    $\vec \nu (\vec \theta )$    needed to achieve any achievable throughput vector    $\vec \theta $    provided that the network has a  chordal  conflict graph. This class of networks contains a number of special cases of interest such as (inhomogeneous) line networks and networks with an acyclic conflict graph. Moreover, these back-off rates are such that the back-off rate of a node only depends on its own target throughput and the target throughput of its neighbors and can be determined in a  distributed  manner. We further indicate that back-off rates of this form cannot be obtained in general for networks with non-chordal conflict graphs. For general conflict graphs, we nevertheless show how to adapt the back-off rates when a node is added to the network when its interfering nodes form a clique in the conflict graph. Finally, we introduce a distributed chordal approximation algorithm for general conflict graphs, which is shown (using numerical examples) to be more accurate than the Bethe approximation.

Connected through hyperedge HE-1 (induced by source paper "efficient csma using regional free energy approximations"):
  with members:
    - "free energy approximations for csma networks"
    - "adaptive csma under the sinr model efficient approximation algorithms for throughput and utility maximization"
    - "csma using the bethe approximation scheduling and utility maximization"
    - "throughput optimal random access with order optimal delay"
    - "applications of belief propagation in csma wireless networks"
    - "fast mixing of parallel glauber dynamics and low delay csma scheduling"
    - "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 "free energy approximations for csma networks"):
  with members:
    - "efficient csma using regional free energy approximations"
    - "adaptive csma under the sinr model efficient approximation algorithms for throughput and utility maximization"
    - "csma using the bethe approximation scheduling and utility maximization"
    - "applications of belief propagation in csma wireless networks"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in 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)