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: "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"
  Abstract: A cognitive radio network (CRN) is formed by either allowing the secondary users (SUs) in a secondary communication network (SCN) to opportunistically operate in the frequency bands originally allocated to a primary communication network (PCN) or by allowing SCN to coexist with the primary users (PUs) in PCN as long as the interference caused by SCN to each PU is properly regulated. In this paper, we consider the latter case, known as spectrum sharing, and study the optimal power allocation strategies to achieve the ergodic capacity and the outage capacity of the SU fading channel under different types of power constraints and fading channel models. In particular, besides the interference power constraint at PU, the transmit power constraint of SU is also considered. Since the transmit power and the interference power can be limited either by a peak or an average constraint, various combinations of power constraints are studied. It is shown that there is a capacity gain for SU under the average over the peak transmit/interference power constraint. It is also shown that fading for the channel between SU transmitter and PU receiver is usually a beneficial factor for enhancing the SU channel capacities.

Connected through hyperedge HE-1 (induced by source paper "optimal power control for over the air computation in fading channels"):
  with members:
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "over the air function computation in sensor networks"
    - "on ergodic sum capacity of fading cognitive multiple access and broadcast channels"
    - "compute and forward harnessing interference through structured codes"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-2 (induced by source paper "asymptotic performance analysis of generalized user selection for interference limited multiuser secondary networks"):
  with members:
    - "on the asymptotic throughput of the k th best secondary user selection in cognitive radio systems"
    - "investigation on multiuser diversity in spectrum sharing based cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "riding on the primary a new spectrum sharing paradigm for wireless powered iot devices"):
  with members:
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "cost minimization for fading channels with energy harvesting and conventional energy"
    - "full duplex wireless powered communication network with energy causality"
    - "simultaneous information and power transfer for broadband wireless systems"

Connected through hyperedge HE-4 (induced by source paper "resource allocation based on deep neural networks for cognitive radio networks"):
  with members:
    - "echo state networks for proactive caching in cloud based radio access networks with mobile users"
    - "why deep neural networks for function approximation"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "on the asymptotic throughput of the k th best secondary user selection in cognitive radio systems"):
  with members:
    - "mimo wireless communications under statistical queueing constraints"

Connected through hyperedge HE-6 (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-7 (induced by source paper "optimizing df cognitive radio networks with full duplex enabled energy access points"):
  with members:
    - "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model"
    - "wireless powered communication networks an overview"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "cognitive wireless powered network spectrum sharing models and throughput maximization"
    - "wireless powered cooperative jamming for secrecy multi af relaying networks"
    - "comp meets smart grid a new communication and energy cooperation paradigm"
    - "energy efficient resource allocation for wireless powered communication networks"
    - "joint resource optimization for multicell networks with wireless energy harvesting relays"

Connected through hyperedge HE-8 (induced by source paper "on the performance analysis of underlay cognitive radio systems a deployment perspective"):
  with members:
    - "sensing throughput tradeoff for interweave cognitive radio system a deployment centric viewpoint"
    - "estimation throughput tradeoff for underlay cognitive radio systems"
    - "the effects of limited channel knowledge on cognitive radio system capacity"
    - "a unified framework for the ergodic capacity of spectrum sharing cognitive radio systems"
    - "channel capacity limits of cognitive radio in asymmetric fading environments"

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)