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: "coalitional graph games for popular content distribution in cognitive radio vanets"
  Abstract: Popular content distribution is one of the key services provided by vehicular ad hoc networks (VANETs), in which a popular file is broadcasted by roadside units (RSUs) to the on-board units (OBUs) driving through a particular area. Due to fast speed and deep fading, some file packets might be lost during the vehicle-to-roadside broadcasting stage. In this paper, we propose a peer-to-peer (P2P) approach to allow the OBUs to exchange data and complement the missing packets. Specifically, we introduce a coalitional graph game to model the cooperation among OBUs and propose a coalition formation algorithm to implement the P2P approach. Moreover, cognitive radio is utilized for vehicle-to-vehicle transmissions so that the P2P approach does not require additional bandwidth. Simulation results show that the proposed approach performs better in various conditions, relative to the non-cooperative approach, in which the OBUs share no information and simply response to any data request from other OBUs.

Connected through hyperedge HE-1 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "deep learning for wireless physical layer opportunities and challenges"

Connected through hyperedge HE-2 (induced by source paper "throughput of infrastructure based cooperative vehicular networks"):
  with members:
    - "towards a simple relationship to estimate the capacity of static and mobile wireless networks"

Connected through hyperedge HE-3 (induced by source paper "overlapping coalition formation games for emerging communication networks"):
  with members:
    - "what will 5g be"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"
    - "hedonic coalition formation for distributed task allocation among wireless agents"
    - "cooperative games with overlapping coalitions"
    - "coalition formation games for distributed cooperation among roadside units in vehicular networks"
    - "physical layer security coalitional games for distributed cooperation"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "a distributed merge and split algorithm for fair cooperation in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "an efficient mac protocol with selective grouping and cooperative sensing in cognitive radio networks"):
  with members:
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "cognitive radio an information theoretic perspective"

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.GT(Computer Science and Game Theory)