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 "achievable rates and outer bound for the half duplex mac with generalized feedback"):
  Abstract: This paper provides comprehensive coding and outer bound for the half-duplex multiple access channel with generalized feedback (MAC-GF). Two users communicate with one destination over a discrete memoryless channel using time division. Each transmission block is divided into 3 time slots with variable durations: the destination is always in receive mode, while each user alternatively transmits and receives during the first 2 time slots, then both cooperate to send information during the last one. The paper proposes two decode-forward based coding schemes, analyzes their rate regions, and also derives two outer bounds with rate constraints similar to the achievable regions. Both schemes requires no block Makovity, allowing the destination to decode at the end of each block without any delay. In the first scheme, the codewords in the third time slot are superimposed on the codewords of the first two, whereas in the second scheme, these codewords are independent. While the second scheme is simpler, the first scheme helps emphasize the importance of joint decoding over separate decoding among multiple time slots at the destination. For the Gaussian channel, the two schemes with joint decoding are equivalent, as are the two outer bounds. For physically degraded Gaussian channels, the proposed schemes achieve the capacity. Extension to the m-user half-duplex MAC-GF are provided. Numerical results for the Gaussian channel shows significant rate region improvement over the classical MAC and that the outer bound becomes increasingly tight as the inter-user link quality increases

Includes members:
  Member "a half duplex cooperative scheme with partial decode forward relaying":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uplink user assisted relaying in cellular networks")
  Member "dependence balance based outer bounds for gaussian networks with cooperation and feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint state sensing and communication over memoryless multiple access channels")
  Member "capacity bounds of half duplex gaussian cooperative interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative relaying under spatially and temporally correlated interference")

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)