human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "decentralized constraint satisfaction"):
  Abstract: We show that several important resource allocation problems in wireless networks fit within the common framework of constraint satisfaction problems (CSPs). Inspired by the requirements of these applications, where variables are located at distinct network devices that may not be able to communicate but may interfere, we define natural criteria that a CSP solver must possess in order to be practical. We term these algorithms decentralized CSP solvers. The best known CSP solvers were designed for centralized problems and do not meet these criteria. We introduce a stochastic decentralized CSP solver, proving that it will find a solution in almost surely finite time, should one exist, and also showing it has many practically desirable properties. We benchmark the algorithm's performance on a well-studied class of CSPs, random k-SAT, illustrating that the time the algorithm takes to find a satisfying assignment is competitive with stochastic centralized solvers on problems with order a thousand variables despite its decentralized nature. We demonstrate the solver's practical utility for the problems that motivated its introduction by using it to find a noninterfering channel allocation for a network formed from data from downtown Manhattan.

Includes members:
  Member "decentralised learning macs for collision free access in wlans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cost sharing games for energy efficient multi hop broadcast in wireless networks")
  Member "towards a collision free wlan dynamic parameter adjustment in csma e2ca":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a high efficiency mac protocol for wlans providing fairness in dense scenarios")
  Member "circumspect descent prevails in solving random constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bose einstein condensation in satisfiability problems")
  Member "threshold values of random k sat from the cavity method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical decomposition mechanism by konig egervary layer subgraph with application on vertex cover")
  Member "survey propagation an algorithm for satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)