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: "energy efficient 5g outdoor to indoor communication sudas over licensed and unlicensed spectrum"
  Abstract: In this paper, we study the joint resource allocation algorithm design for downlink and uplink multicarrier transmission assisted by a shared user equipment (UE)-side distributed antenna system (SUDAS). The proposed SUDAS simultaneously utilizes licensed frequency bands and unlicensed frequency bands, (e.g. millimeter wave bands), to enable a spatial multiplexing gain for single-antenna UEs to improve energy efficiency and system throughput of $5$-th generation (5G) outdoor-to-indoor communication. The design of the UE selection, the time allocation to uplink and downlink, and the transceiver processing matrix is formulated as a non-convex optimization problem for the maximization of the end-to-end system energy efficiency (bits/Joule). The proposed problem formulation takes into account minimum data rate requirements for delay sensitive UEs and the circuit power consumption of all transceivers. In order to design a tractable resource allocation algorithm, we first show that the optimal transmitter precoding and receiver post-processing matrices jointly diagonalize the end-to-end communication channel for both downlink and uplink communication via SUDAS. Subsequently, the matrix optimization problem is converted to an equivalent scalar optimization problem for multiple parallel channels, which is solved by an asymptotically globally optimal iterative algorithm. Besides, we propose a suboptimal algorithm which finds a locally optimal solution of the non-convex optimization problem. Simulation results illustrate that the proposed resource allocation algorithms for SUDAS achieve a significant performance gain in terms of system energy efficiency and spectral efficiency compared to conventional baseline systems by offering multiple parallel data streams for single-antenna UEs.

Connected through hyperedge HE-1 (induced by source paper "enhancing cellular performance through device to device distributed mimo"):
  with members:
    - "optimal resource allocation for power efficient mc noma with imperfect channel state information"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "on the minimum decoding delay of balanced complex orthogonal designs"
    - "what will 5g be"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"
    - "complex orthogonal designs with forbidden 2 times 2 submatrices"

Connected through hyperedge HE-2 (induced by source paper "joint millimeter wave and microwave resources allocation in cellular networks with dual mode base stations"):
  with members:
    - "context aware scheduling of joint millimeter wave and microwave resources for dual mode base stations"
    - "optimal traffic aggregation in multi rat heterogeneous wireless networks"
    - "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"
    - "local multipath model parameters for generating 5g millimeter wave 3gpp like channel impulse response"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "millimeter wave cellular networks a mac layer perspective"
    - "an mdp model for optimal handover decisions in mmwave cellular networks"
    - "matching theory for priority based cell association in the downlink of wireless small cell networks"

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)