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 interference networks over finite fields"):
  Abstract: We present a framework to study linear deterministic interference networks over finite fields. Unlike the popular linear deterministic models introduced to study Gaussian networks, we consider networks where the channel coefficients are general scalars over some extension field $\FF_{p^m}$ (scalar $m$-th extension-field models), $m \times m$ diagonal matrices over $\FF_p$ ($m$-symbol extension ground-field models), and $m \times m$ general non-singular matrices (MIMO ground field models). We use the companion matrix representation of the extension field to convert $m$-th extension scalar models into MIMO ground-field models where the channel matrices have special algebraic structure. For such models, we consider the $2 \times 2 \times 2$ topology (two-hops two-flow) and the 3-user interference network topology. We derive achievability results and feasibility conditions for certain schemes based on the Precoding-Based Network Alignment (PBNA) approach, where intermediate nodes use random linear network coding (i.e., propagate random linear combinations of their incoming messages) and non-trivial precoding/decoding is performed only at the network edges, at the sources and destinations. Furthermore, we apply this approach to the scalar $2\times 2\times 2$ complex Gaussian IC with fixed channel coefficients, and show two competitive schemes outperforming other known approaches at any SNR, where we combine finite-field linear precoding/decoding with lattice coding and the Compute and Forward approach at the signal level. As a side result, we also show significant advantages of vector linear network coding both in terms of feasibility probability (with random coding coefficients) and in terms of coding latency, with respect to standard scalar linear network coding, in PBNA schemes.

Includes members:
  Member "two hop interference channels impact of linear schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dof analysis in a two layered heterogeneous wireless interference network")
  Member "real interference alignment exploiting the potential of single antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi antenna coded content delivery with caching a low complexity solution")
  Member "two hop interference channels impact of linear time varying schemes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two hop interference channels impact of linear schemes")
  Member "on the capacity of the finite field counterparts of wireless interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an achievable rate region for the three user interference channel based on coset codes")
  Member "structured lattice codes for some two user gaussian networks with cognition coordination and two hops":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two hop interference channels impact of linear schemes")
  Member "the awgn red alert problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime")
  Member "compute and forward strategies for cooperative distributed antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private information retrieval over gaussian mac")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "best relay selection in gaussian half duplex diamond 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)