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 channel recommendation for opportunistic spectrum access"
  Abstract: We propose a dynamic spectrum access scheme where secondary users recommend "good" channels to each other and access accordingly. We formulate the problem as an average reward based Markov decision process. We show the existence of the optimal stationary spectrum access policy, and explore its structure properties in two asymptotic cases. Since the action space of the Markov decision process is continuous, it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, value iteration, or Q-learning methods. Instead, we propose a new algorithm based on the Model Reference Adaptive Search method, and prove its convergence to the optimal policy. Numerical results show that the proposed algorithms achieve up to 18% and 100% performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics.

Connected through hyperedge HE-1 (induced by source paper "amazon in the white space social recommendation aided distributed spectrum access"):
  with members:
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "imitation based social spectrum sharing"
    - "spatial spectrum access game"
    - "smart streaming for online video services"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-2 (induced by source paper "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"):
  with members:
    - "database assisted distributed spectrum sharing"
    - "stable nash equilibria of medium access games under symmetric socially altruistic behavior"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"
    - "altruism in atomic congestion games"

Connected through hyperedge HE-3 (induced by source paper "quality of service games for spectrum sharing"):
  with members:
    - "distributed spectrum access with spatial reuse"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"

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.DC(Distributed, Parallel, and Cluster Computing)