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: "interference alignment for the k user interference channel with imperfect csi"
  Abstract: In this paper we explore the information-theoretic aspects of interference alignment and its relation to channel state information (CSI). For the $K-$user interference channel using different changing patterns between different users, we propose several methods to align some parts of interferences and to increase what is achieved by time sharing method. For more practical case when all the channel links connected to the same destination have the same changing pattern, we find an upper-bound and analyze it for the large interference channel network. This result shows that when the size of the network increases, the upper-bound value goes to $\frac{\sqrt{K}}{2}$. For the fast fading channel when all the channels have the same changing pattern, we show that when the direct links have different characteristic functions (channel permutation or memory), in the absence of half part of CSI (cross links) at both transmitters and receivers, one can achieve $K/2$ degrees-of-freedom (DoF). Also by the converse proof we show that this is the minimum channel information to achieve maximum DoF of $\frac{K}{2}$. Throughout this work, this fact has been pinpointed to prove statements about more general partial state CSI and achievable DoF. In other words, for the 3-user fully connected interference channel we find out while $\frac{3}{2}$ lies in achievable DoF, we don't need to know half part of the CSI. Also, the result has been extended to a more general form for $K-$user interference channel and through the converse proof, its functionality on channel state is proved to be optimum.

Connected through hyperedge HE-1 (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:
    - "adaptive interference alignment with csi uncertainty"
    - "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-2 (induced by source paper "a new approach for throughput enhancement of mimo interference networks under imperfect channel state information"):
  with members:
    - "group based interference alignment"
    - "on the beamforming design for efficient interference alignment"

Connected through hyperedge HE-3 (induced by source paper "sum degrees of freedom of the k user interference channel with blind csi"):
  with members:
    - "achievable degrees of freedom of the k user siso interference channel with blind interference alignment using staggered antenna switching"
    - "completely stale transmitter channel state information is still very useful"

Connected through hyperedge HE-4 (induced by source paper "degrees of freedom rate region of the k user interference channel with blind csit using staggered antenna switching"):
  with members:
    - "achievable degrees of freedom of the k user siso interference channel with blind interference alignment using staggered antenna switching"

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)