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 degrees of freedom of siso x networks with synergistic alternating channel state information at transmitters"):
  Abstract: In this paper, we consider the two-user single-input single-output (SISO) X-channel and $K$-user SISO X-network in fast fading environment. It is assumed that the transmitters have access to synergistic alternating channel state information (CSI). Specifically, the CSIT alternates between three states, namely, perfect, delayed and no-CSIT, in a certain manner to enable these states to work together cooperatively. These states are associated with fractions of time denoted by $\lambda_P$, $\lambda_D$ \text{and} $\lambda_N$, respectively. For the two-user $X$-channel, simple upper bound is developed to prove the tightness of the achievability result of $4/3$ DoF under a certain distribution of the availability of three CSIT states for $\Lambda(\lambda_P=1/3, \lambda_D= 1/3, \lambda_N=1/3)$. For the $K$-user $X$-network, it is shown that the sum Degrees of freedom (DoF) is at least $2K/(K + 1)$, using two-phase transmission schemes over finite symbols channel extension and under the same distribution of the availability of $\Lambda(\lambda_P=1/3, \lambda_D= 1/3, \lambda_N=1/3)$.This achievability result, can be considered as a tight lower bound, coincides with the best lower bound known for the same network but with partial output feedback in stead of alternating CSIT. Hence, we show that the role of synergistic alternating CSIT with distribution $\Lambda(1/3,1/3,1/3)$ is equivalent to the partial output feedback. Also, this lower bound is strictly better than the best lower bound known for the case of delayed CSI assumption for all values of $K$. All the proposed transmission schemes are based on two phases transmission strategy, namely, interference creation and interference resurrection, which exploit the synergy of instantaneous CSI and delay CSIT to retrospectively align interference in the subsequent channel uses.

Includes members:
  Member "on x channels with feedback and delayed csi":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the degrees of freedom of mimo x networks with non cooperation transmitters")
  Member "completely stale transmitter channel state information is still very useful":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achievable dof regions of three user mimo broadcast channel with delayed csit")

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)