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 "a unified relay framework with both d f and c f relay nodes"):
  Abstract: Decode-and-forward (D-F) and compress-and-forward (C-F) are two fundamentally different relay strategies proposed by (Cover and El Gamal, 1979). Individually, either of them has been successfully generalized to multi-relay channels. In this paper, to allow each relay node the freedom of choosing either of the two strategies, we propose a unified framework, where both the D-F and C-F strategies can be employed simultaneously in the network. It turns out that, to fully incorporate the advantages of both the best known D-F and C-F strategies into a unified framework, the major challenge arises as follows: For the D-F relay nodes to fully utilize the help of the C-F relay nodes, decoding at the D-F relay nodes should not be conducted until all the blocks have been finished; However, in the multi-level D-F strategy, the upstream nodes have to decode prior to the downstream nodes in order to help, which makes simultaneous decoding at all the D-F relay nodes after all the blocks have been finished inapplicable. To tackle this problem, nested blocks combined with backward decoding are used in our framework, so that the D-F relay nodes at different levels can perform backward decoding at different frequencies. As such, the upstream D-F relay nodes can decode before the downstream D-F relay nodes, and the use of backward decoding at each D-F relay node ensures the full exploitation of the help of both the other D-F relay nodes and the C-F relay nodes. The achievable rates under our unified relay framework are found to combine both the best known D-F and C-F achievable rates and include them as special cases.

Includes members:
  Member "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity approximations for gaussian relay networks")
  Member "selective coding strategy for unicast composite networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed noisy network coding and cooperative unicasting in wireless networks")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "short message quantize forward network coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity approximations for gaussian relay networks")
  Member "on the optimal compressions in the compress and forward relay schemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes")
  Member "noisy network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an information theoretic analysis of the gaussian multicast channel with interactive user cooperation")
  Member "lecture notes on network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the required number of antennas in a point to point large but finite mimo system outage limited scenario")
  Member "an improvement of cover el gamal s compress and forward relay scheme":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the optimal compressions in the compress and forward relay schemes")

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)