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 "cognitive wyner networks with clustered decoding"):
  Abstract: We study an interference network where equally-numbered transmitters and receivers lie on two parallel lines, each transmitter opposite its intended receiver. We consider two short-range interference models: the "asymmetric network," where the signal sent by each transmitter is interfered only by the signal sent by its left neighbor (if present), and a "symmetric network," where it is interfered by both its left and its right neighbors. Each transmitter is cognizant of its own message, the messages of the $t_\ell$ transmitters to its left, and the messages of the $t_r$ transmitters to its right. Each receiver decodes its message based on the signals received at its own antenna, at the $r_\ell$ receive antennas to its left, and the $r_r$ receive antennas to its right. For such networks we provide upper and lower bounds on the multiplexing gain, i.e., on the high-SNR asymptotic logarithmic growth of the sum-rate capacity. In some cases our bounds meet, e.g., for the asymmetric network. Our results exhibit an equivalence between the transmitter side-information parameters $t_\ell, t_r$ and the receiver side-information parameters $r_\ell, r_r$ in the sense that increasing/decreasing $t_\ell$ or $t_r$ by a positive integer $\delta$ has the same effect on the multiplexing gain as increasing/decreasing $r_\ell$ or $r_r$ by $\delta$. Moreover---even in asymmetric networks---there is an equivalence between the left side-information parameters $t_\ell, r_\ell$ and the right side-information parameters $t_r, r_r$.

Includes members:
  Member "on optimal message assignments for interference channels with comp transmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "degrees of freedom of interference channels with comp transmission and reception")
  Member "degrees of freedom dof of locally connected interference channels with coordinated multi point comp transmission":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wyner s network on caches combining receiver caching with a flexible backhaul")
  Member "degrees of freedom of interference channels with comp transmission and reception":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "degrees of freedom of cache aided wireless cellular networks")
  Member "fundamental limits of cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "interference channels with coordinated multi point transmission degrees of freedom message assignment and fractional reuse":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "topological interference management with transmitter cooperation")
  Member "interference cooperation and connectivity a degrees of freedom perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interference networks with no csit impact of topology")
  Member "on the sum capacity of k user cascade gaussian z interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "channel conditions for the optimality of interference decoding schemes for k user gaussian interference channels")

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)