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: "hybrid centralized distributed resource allocation for device to device communication underlaying cellular networks"
  Abstract: The basic idea of device-to-device (D2D) communication is that pairs of suitably selected wireless devices reuse the cellular spectrum to establish direct communication links, provided that the adverse effects of D2D communication on cellular users are minimized and that cellular users are given higher priority in using limited wireless resources. Despite its great potential in terms of coverage and capacity performance, implementing this new concept poses some challenges, particularly with respect to radio resource management. The main challenges arise from a strong need for distributed D2D solutions that operate in the absence of precise channel and network knowledge. To address this challenge, this paper studies a resource allocation problem in a single-cell wireless network with multiple D2D users sharing the available radio frequency channels with cellular users. We consider a realistic scenario where the base station (BS) is provided with strictly limited channel knowledge, whereas D2D and cellular users have no information. We prove a lower bound for the cellular aggregate utility in the downlink with fixed BS power, which allows for decoupling the channel allocation and D2D power control problems. An efficient graph-theoretical approach is proposed to perform channel allocation, which offers flexibility with respect to allocation criteria (aggregate utility maximization, fairness, and quality-of-service (QoS) guarantee). We model the power control problem as a multiagent learning game. We show that the game is an exact potential game with noisy rewards, which is defined on a discrete strategy set, and characterize the set of Nash equilibria. Q-learning better-reply dynamics is then used to achieve equilibrium.

Connected through hyperedge HE-1 (induced by source paper "sector based radio resource allocation sbrra algorithm for better quality of service and experience in device to device d2d communication"):
  with members:
    - "optimal resource allocation in multicast device to device communications underlaying lte networks"
    - "limited feedback scheme for device to device communications in 5g cellular networks with reliability and cellular secrecy outage constraints"
    - "device to device communication in cellular networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-2 (induced by source paper "when distributed outperforms centralized scheduling in d2d enabled cellular networks"):
  with members:
    - "energy efficient resource allocation for device to device underlay communication"
    - "performance analysis of network assisted two hop d2d communications"
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "resource optimization in device to device cellular systems using time frequency hopping"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-3 (induced by source paper "a cascaded channel power allocation for d2d underlaid cellular networks using matching theory"):
  with members:
    - "distributed resource allocation in d2d enabled multi tier cellular networks an auction approach"
    - "a survey on device to device communication in cellular networks"
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "matching theory for future wireless networks fundamentals and applications"
    - "peer effects and stability in matching markets"

Connected through hyperedge HE-4 (induced by source paper "distributed vs centralized scheduling in d2d enabled cellular networks"):
  with members:
    - "energy efficient resource allocation for device to device underlay communication"
    - "performance analysis of network assisted two hop d2d communications"
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-5 (induced by source paper "optimal distributed channel assignment in d2d networks using learning in noisy potential games"):
  with members:
    - "optimal qos aware channel assignment in d2d communications with partial csi"
    - "a survey on device to device communication in cellular networks"
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-6 (induced by source paper "resource allocation for d2d communications with partial channel state information"):
  with members:
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-7 (induced by source paper "optimal qos aware channel assignment in d2d communications with partial csi"):
  with members:
    - "qos aware joint mode selection and channel assignment for d2d communications"
    - "a survey on device to device communication in cellular networks"
    - "spectrum sharing for device to device communication in cellular networks"
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "what will 5g be"
    - "emerging technologies and research challenges for 5g wireless networks"
    - "five disruptive technology directions for 5g"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-8 (induced by source paper "bio inspired resource allocation for relay aided device to device communications"):
  with members:
    - "distributed resource allocation for relay aided device to device communication a message passing approach"

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)