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 "codebook based opportunistic interference alignment"):
  Abstract: For the multiple-input multiple-output interfering multiple-access channels (IMACs), opportunistic interference alignment (OIA) using the singular value decomposition (SVD)-based beamforming at each user fundamentally reduces the user scaling condition required to achieve any target DoF, compared to that for the single-input multiple-output IMAC. In this paper, we tackle two practical challenges of the existing SVD-based OIA: 1) the need of full feedforward of the selected users’ beamforming weight vectors and 2) a low rate achieved based on the exiting zero-forcing receiver. We first propose a codebook-based OIA, in which the weight vectors are chosen from a predefined codebook with a finite size so that information of the weight vectors can be sent to the belonging BS with limited feedforward. We derive the codebook size required to achieve the same user scaling condition as the SVD-based OIA case for both Grassmannian and random codebooks. Surprisingly, it is shown that the derived codebook size is the same for the two codebook methods. Second, we introduce an enhanced receiver at the base stations (BSs) in pursuit of further improving the achievable rate. Assuming no collaboration between the BSs, the interfering links between a BS and the selected users in neighboring cells are difficult to be acquired at the belonging BS. We propose the use of a simple minimum Euclidean distance receiver operating with no information of the interfering links. With the help of the OIA, we show that this new receiver asymptotically achieves the channel capacity as the number of users increases.

Includes members:
  Member "on the achievable dof and user scaling law of opportunistic interference alignment in 3 transmitter mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")
  Member "opportunistic interference alignment for mimo interfering multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi stream opportunistic network decoupling relay selection and interference management")
  Member "multi cell random beamforming achievable rate and degrees of freedom region":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi stream opportunistic network decoupling relay selection and interference management")
  Member "opportunistic interference mitigation achieves optimal degrees of freedom in wireless multi cell uplink networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")
  Member "channel estimation and linear precoding in multiuser multiple antenna tdd systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint beamforming and power allocation in downlink noma multiuser mimo networks")
  Member "interference alignment under limited feedback for mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")
  Member "interference alignment with limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")
  Member "pilot contamination and precoding in multi cell tdd systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)