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: "a game theoretic perspective on self organizing optimization for cognitive small cells"
  Abstract: In this article, we investigate self-organizing optimization for cognitive small cells (CSCs), which have the ability to sense the environment, learn from historical information, make intelligent decisions, and adjust their operational parameters. By exploring the inherent features, some fundamental challenges for self-organizing optimization in CSCs are presented and discussed. Specifically, the dense and random deployment of CSCs brings about some new challenges in terms of scalability and adaptation; furthermore, the uncertain, dynamic, and incomplete information constraints also impose some new challenges in terms of convergence and robustness. For providing better service to users and improving resource utilization, four requirements for self-organizing optimization in CSCs are presented and discussed. Following the attractive fact that the decisions in game-theoretic models are exactly coincident with those in self-organizing optimization (i.e., distributed and autonomous), we establish a framework of gametheoretic solutions for self-organizing optimization in CSCs and propose some featured game models. Specifically, their basic models are presented, some examples are discussed, and future research directions are given.

Connected through hyperedge HE-1 (induced by source paper "cyber physical systems resilience state of the art research issues and future trends"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "mitigating jamming attacks using energy harvesting"
    - "fundamental concepts of cyber resilience introduction and overview"
    - "tarco two stage auction for d2d relay aided computation resource allocation in hetnet"
    - "an introduction to deep learning for the physical layer"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "sustainable incentives for mobile crowdsensing"

Connected through hyperedge HE-2 (induced by source paper "stackelberg game approaches for anti jamming defence in wireless networks"):
  with members:
    - "defeating jamming with the power of silence a game theoretic analysis"

Connected through hyperedge HE-3 (induced by source paper "load aware dynamic spectrum access for small cell networks a graphical game approach"):
  with members:
    - "partially distributed resource allocation in small cell networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "full duplex cloud radio access networks an information theoretic viewpoint"
    - "ergodic capacity analysis of remote radio head associations in cloud radio access networks"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic spectrum access in time varying environment distributed learning beyond expectation optimization"):
  with members:
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"
    - "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "adaptive channel allocation spectrum etiquette for cognitive radio networks"

Connected through hyperedge HE-5 (induced by source paper "database assisted spectrum access in dynamic networks a distributed learning solution"):
  with members:
    - "a hybrid pricing framework for tv white space database"
    - "database assisted distributed spectrum sharing"

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.IT(Information Theory)