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 "adaptation is useless for two discrete additive noise two way channels"):
  Abstract: In two-way channels, each user transmits and receives at the same time. This allows each encoder to interactively adapt the current input to its own message and all previously received signals. Such coding approach can introduce correlation between inputs of different users, since all the users' outputs are correlated by the nature of the channel. However, for some channels, such adaptation in the coding scheme and its induced correlation among users are useless in the sense that they do not help enlarge the capacity region with respect to the standard coding method (where each user encodes only based on its own message). In this paper, it is shown that adaptation is not helpful for enlarging the capacity region of two classes of two-way discrete channels: the modulo additive-noise channel with memory and the multiple access/degraded broadcast channel.

Includes members:
  Member "two way networks when adaptation is useless":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "classes of full duplex channels with capacity achieved without adaptation")
  Member "a coding theorem for a class of stationary channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "finite state channels with time invariant deterministic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")

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)