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 "linear network coding for multiple groupcast sessions an interference alignment approach"):
  Abstract: We consider the problem of linear network coding over communication networks, representable by directed acyclic graphs, with multiple groupcast sessions: the network comprises of multiple destination nodes, each desiring messages from multiple sources. We adopt an interference alignment perspective, providing new insights into designing practical network coding schemes as well as the impact of network topology on the complexity of the alignment scheme. In particular, we show that under certain (polynomial-time checkable) constraints on networks with $K$ sources, it is possible to achieve a rate of $1/(L+d+1)$ per source using linear network coding coupled with interference alignment, where each destination receives messages from $L$ sources ($L < K$), and $d$ is a parameter, solely dependent on the network topology, that satisfies $0 \leq d < K-L$.

Includes members:
  Member "topological interference management through index coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective")
  Member "on the feasibility of precoding based network alignment for three unicast sessions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi server coded caching")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "network coding for multiple unicasts an interference alignment approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "alignment based network coding for two unicast z networks")
  Member "coding for errors and erasures in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")
  Member "interference alignment and the degrees of freedom for the k user interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on off switched interference alignment for diversity multiplexing tradeoff improvement in the 2 user x network with two antennas")

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)