human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the overhead of interference alignment training feedback and cooperation"):
  Abstract: Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

Includes members:
  Member "on the degrees of freedom achievable through interference alignment in a mimo interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "centralized and decentralized cache aided interference management in heterogeneous parallel channels")
  Member "fundamental limits of cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "user partitioning for less overhead in mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed long term base station clustering in cellular networks using coalition formation")
  Member "cooperative algorithms for mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the capacity and optimal signaling for gaussian mimo channels under interference constraints full version")
  Member "optimization of training and feedback overhead for beamforming over block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback")
  Member "interference alignment with analog channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the feasibility of interference alignment in compounded mimo broadcast channels with antenna correlation and mixed user classes")
  Member "interference alignment under limited feedback for mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")
  Member "interference alignment with limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")

Question: Please tell me which class the source paper 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)