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: "applications of belief propagation in csma wireless networks"
  Abstract: The belief propagation (BP) algorithm is an efficient way to solve "inference" problems in graphical models, such as Bayesian networks and Markov random fields. The system-state probability distribution of CSMA wireless networks is a Markov random field. An interesting question is how BP can help the analysis and design of CSMA wireless networks. This paper explores three such applications. First, we show how BP can be used to compute the throughputs of different links in the network given their access intensities, defined as the mean packet transmission time divided by the mean backoff countdown time. Second, we propose an inverse-BP algorithm to solve the reverse problem: how to set the access intensities of different links to meet their target throughputs? Third, we introduce a BP-adaptive CSMA algorithm to find the link access intensities that can achieve optimal system utility. BP solves the three problems with exact results in tree networks. It may, however, lose accuracy in networks with a loopy contention graph. We show how a generalized version of BP, GBP, can be designed to solve the three problems with high accuracy for networks with a loopy contention graph. Importantly, we show how the BP and GBP algorithms in this paper can be implemented in a distributed manner, making them useful in practical CSMA network opera-tion.

Connected through hyperedge HE-1 (induced by source paper "efficient csma using regional free energy approximations"):
  with members:
    - "explicit back off rates for achieving target throughputs in csma ca networks"
    - "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"
    - "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:
    - "explicit back off rates for achieving target throughputs in csma ca networks"
    - "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"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-3 (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"
    - "adaptive spatial aloha fairness and stochastic geometry"
    - "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-4 (induced by source paper "csma using the bethe approximation scheduling and utility maximization"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "csma over time varying channels optimality uniqueness and limited backoff rate"
    - "complexity of inference in graphical models"
    - "approaching throughput optimality in distributed csma scheduling algorithms with collisions"
    - "throughput optimal random access with order optimal delay"
    - "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"
    - "randomized scheduling algorithm for queueing networks"

Connected through hyperedge HE-5 (induced by source paper "throughput analysis of csma wireless networks with finite offered load"):
  with members:
    - "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"
    - "effective carrier sensing in csma networks under cumulative interference"
    - "saturation throughput analysis of ieee 802 11 in the presence of non ideal transmission channel and capture effects"
    - "back of the envelope computation of throughput distributions in csma 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)