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: "adaptive interference alignment with csi uncertainty"
  Abstract: Interference alignment (IA) is known to significantly increase sum-throughput at high SNR in the presence of multiple interfering nodes, however, the reliability of IA is little known, which is the subject of this paper. We study the error performance of IA and compare it with conventional orthogonal transmission schemes. Since most IA algorithms require extensive channel state information (CSI), we also investigate the impact of CSI imperfection (uncertainty) on the error performance. Our results show that under identical rates, IA attains a better error performance than the orthogonal scheme for practical signal to noise ratio (SNR) values but is more sensitive to CSI uncertainty. We design bit loading algorithms that significantly improve error performance of the existing IA schemes. Furthermore, we propose an adaptive transmission scheme that not only considerably reduces error probability, but also produces robustness to CSI uncertainty.

Connected through hyperedge HE-1 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends"):
  with members:
    - "asymptotic performance analysis of a k hop amplify and forward relay mimo channel"
    - "iterative distributed minimum total mse approach for secure communications in mimo interference channels"
    - "asymptotic analysis of su mimo channels with transmitter noise and mismatched joint decoding"
    - "secure massive mimo transmission with an active eavesdropper"
    - "interference aligned space time transmission with diversity for the 2 times 2 x network"
    - "space time encoded miso broadcast channel with outdated csit an error rate and diversity performance analysis"
    - "high snr asymptotics of mutual information for discrete constellations with applications to bicm"
    - "differential distributed space time coding with imperfect synchronization in frequency selective channels"

Connected through hyperedge HE-2 (induced by source paper "joint optimization of power splitting and allocation for swipt in interference alignment networks"):
  with members:
    - "interactive joint transfer of energy and information"
    - "simultaneous information and power transfer for broadband wireless systems"
    - "wireless information and power transfer a dynamic power splitting approach"
    - "guiding blind transmitters degrees of freedom optimal interference alignment using relays"
    - "enabling wireless power transfer in cellular networks architecture modeling and deployment"
    - "robust beamforming for wireless information and power transmission"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "mimo broadcasting for simultaneous wireless information and power transfer"

Connected through hyperedge HE-3 (induced by source paper "a time variant channel prediction and feedback framework for interference alignment"):
  with members:
    - "on the degrees of freedom for opportunistic interference alignment with 1 bit feedback the 3 cell case"
    - "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios"
    - "grassmannian differential limited feedback for interference alignment"
    - "ensemble properties of rvq based limited feedback beamforming codebooks"
    - "optimization of training and feedback overhead for beamforming over block fading channels"
    - "interference alignment with analog channel state feedback"
    - "interference alignment under limited feedback for mimo interference channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-4 (induced by source paper "maximizing first order approximate mean of sinr under imperfect channel state information for throughput enhancement of mimo interference networks"):
  with members:
    - "interference alignment for the k user interference channel with imperfect csi"
    - "group based interference alignment"
    - "on the beamforming design for efficient interference alignment"
    - "degrees of freedom of the k user m times n mimo interference channel"

Connected through hyperedge HE-5 (induced by source paper "interference alignment with quantized grassmannian feedback in the k user constant mimo interference channel"):
  with members:
    - "csit sharing over finite capacity backhaul for spatial interference alignment"
    - "on the degrees of freedom achievable through interference alignment in a mimo interference channel"
    - "settling the feasibility of interference alignment for the mimo interference channel the symmetric square case"
    - "on feasibility of interference alignment in mimo interference networks"
    - "downlink interference alignment"
    - "interference alignment with analog channel state feedback"
    - "interference alignment under limited feedback for mimo interference channels"
    - "interference alignment with limited feedback"

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)