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: "sample approximation based deflation approaches for chance sinr constrained joint power and admission control"
  Abstract: Consider the joint power and admission control (JPAC) problem for a multiuser single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, i.e., we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic)-constrained program, where each link’s SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the sample (scenario) approximation scheme to convert them into finitely many simple linear constraints. Furthermore, we reformulate the sample approximation of the chance SINR-constrained JPAC problem as a composite group sparse minimization problem and then approximate it by a second-order cone program (SOCP). The solution of the SOCP approximation can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure (the deflation approach). We exploit the special structure of the SOCP approximation and custom-design an efficient algorithm for solving it. Finally, we illustrate the effectiveness and efficiency of the proposed sample approximation-based deflation approaches by simulations.

Connected through hyperedge HE-1 (induced by source paper "multicast embb and bursty urllc service multiplexing in a comp enabled ran"):
  with members:
    - "ran slicing for massive iot and bursty urllc service multiplexing analysis and optimization"
    - "embb urllc resource slicing a risk sensitive approach"
    - "harmonizing 3gpp and nfv description models providing customized ran slices in 5g networks"
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-2 (induced by source paper "flexible multiple base station association and activation for downlink heterogeneous networks"):
  with members:
    - "joint downlink base station association and power control for max min fairness computation and complexity"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "optimal user cell association for massive mimo wireless networks"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "group sparse beamforming for green cloud ran"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "joint power and admission control based on channel distribution information a novel two timescale approach"):
  with members:
    - "joint power and admission control via linear programming deflation"
    - "a decentralized method for joint admission control and beamforming in coordinated multicell downlink"

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)