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 "reduced complexity index codes and improved upperbound on broadcast rate for neighboring interference problems"):
  Abstract: A single unicast index coding problem (SUICP) with symmetric neighboring interference (SNI) has $K$ messages and $K$ receivers, the $k$th receiver $R_{k}$ wanting the $k$th message $x_{k}$ and having the interference with $D$ messages after and $U$ messages before its desired message. Maleki, Cadambe and Jafar studied SUICP(SNI) because of its importance in topological interference management problems. Maleki \textit{et. al.} derived the lowerbound on the broadcast rate of this setting to be $D+1$. In our earlier work, for SUICP(SNI) with arbitrary $K,D$ and $U$, we defined set $\mathbf{S}$ of 2-tuples and for every $(a,b) \in \mathbf{S}$, we constructed $b$-dimensional vector linear index code with rate $D+1+\frac{a}{b}$ by using an encoding matrix of dimension $Kb \times (b(D+1)+a)$. In this paper, we use the symmetric structure of the SUICP(SNI) to reduce the size of encoding matrix by partitioning the message symbols. The rate achieved in this paper is same as that of the existing constructions of vector linear index codes. More specifically, we construct $b$-dimensional vector linear index codes for SUICP(SNI) by partitioning the $Kb$ messages into $b(U+1)+c$ sets for some non-negative integer $c$. We use an encoding matrix of size $\frac{Kb}{b(U+1)+c} \times \frac{b(D+1)+a}{b(U+1)+c}$ to encode each partition separately. The advantage of this method is that the receivers need to store atmost $\frac{b(D+1)+a}{b(U+1)+c}$ number of broadcast symbols (index code symbols) to decode a given wanted message symbol. We also give a construction of scalar linear index codes for SUICP(SNI) with arbitrary $K,D$ and $U$. We give an improved upperbound on the braodcast rate of SUICP(SNI).

Includes members:
  Member "near optimal vector linear index codes for single unicast index coding problems with symmetric neighboring interference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the broadcast rate of index coding problems with symmetric and consecutive interference")
  Member "low complexity decoding for symmetric neighboring and consecutive side information index coding problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a generalisation of interlinked cycle structures and their index coding capacity")
  Member "capacity of some index coding problems with symmetric neighboring interference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an approximation algorithm for optimal clique cover delivery in coded caching")
  Member "optimal scalar linear index codes for one sided neighboring side information problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rate memory trade off for multi access coded caching with uncoded placement")
  Member "topological interference management through index coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "optimal index codes for a class of multicast networks with receiver side information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cooperative multi sender index coding")

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)