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: "radio resource allocation for device to device underlay communication using hypergraph theory"
  Abstract: Device-to-Device (D2D) communication has been recognized as a promising technique to offload the traffic for the evolved Node B (eNB). However, the D2D transmission as an underlay causes severe interference to both the cellular and other D2D links, which imposes a great technical challenge to radio resource allocation. Conventional graph based resource allocation methods typically consider the interference between two user equipments (UEs), but they cannot model the interference from multiple UEs to completely characterize the interference. In this paper, we study channel allocation using hypergraph theory to coordinate the interference between D2D pairs and cellular UEs, where an arbitrary number of D2D pairs are allowed to share the uplink channels with the cellular UEs. Hypergraph coloring is used to model the cumulative interference from multiple D2D pairs, and thus, eliminate the mutual interference. Simulation results show that the system capacity is significantly improved using the proposed hypergraph method in comparison to the conventional graph based one.

Connected through hyperedge HE-1 (induced by source paper "learning to branch accelerating resource allocation in wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "a survey on device to device communication in cellular networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "energy efficient power and channel allocation in underlay device to multi device communications"):
  with members:
    - "system level modeling and optimization of the energy efficiency in cellular networks a stochastic geometry framework"
    - "optimal resource allocation in multicast device to device communications underlaying lte networks"
    - "a tractable framework for the analysis of dense heterogeneous cellular networks"
    - "energy efficient power control a look at 5g wireless technologies"
    - "gaussian approximation for the downlink interference in heterogeneous cellular networks"
    - "device to device millimeter wave communications interference coverage rate and finite topologies"
    - "modeling and performance analysis of clustered device to device networks"
    - "an analytical framework for device to device communication in cellular networks"

Connected through hyperedge HE-3 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology"):
  with members:
    - "user association in 5g networks a survey and an outlook"
    - "optimal joint power and subcarrier allocation for full duplex multicarrier non orthogonal multiple access systems"
    - "optimal dynamic point selection for power minimization in multiuser downlink comp"
    - "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty"
    - "low complexity delay constrained beamforming for multi user mimo systems with imperfect csit"
    - "robust monotonic optimization framework for multicell miso systems"
    - "worst case sinr constrained robust coordinated beamforming for multicell wireless systems"
    - "probabilistic sinr constrained robust transmit beamforming a bernstein type inequality based conservative approach"

Connected through hyperedge HE-4 (induced by source paper "on some distributed scheduling algorithms for wireless networks with hypergraph interference models"):
  with members:
    - "performance of sufficient conditions for distributed quality of service support in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "load balancing for 5g ultra dense networks using device to device communications"):
  with members:
    - "ultra dense small cell networks turning density into energy efficiency"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-6 (induced by source paper "hypergraph theory applications in 5g heterogeneous ultra dense networks"):
  with members:
    - "overlapping coalition formation games for emerging communication networks"

Connected through hyperedge HE-7 (induced by source paper "d2d u device to device communications in unlicensed bands for 5g and beyond"):
  with members:
    - "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "unlicensed lte wifi coexistence is lbt inherently fairer than csat"
    - "an energy efficient mac protocol for fully connected wireless ad hoc networks"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based 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.NI(Networking and Internet Architecture)