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 "wyner ziv type versus noisy network coding for a state dependent mac"):
  Abstract: We consider a two-user state-dependent multiaccess channel in which the states of the channel are known non-causally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states non-causally transmits an individual message. We find explicit characterizations of the capacity region of this communication model. The analysis also reveals optimal ways of exploiting the knowledge of the state only strictly causally at the encoder that sends only the common message when such a knowledge is beneficial. The encoders collaborate to convey to the decoder a lossy version of the state, in addition to transmitting the information messages through a generalized Gel'fand-Pinsker binning. Particularly important in this problem are the questions of 1) optimal ways of performing the state compression and 2) whether or not the compression indices should be decoded uniquely. We show that both compression a-la noisy network coding, i.e., with no binning, and compression using Wyner-Ziv binning are optimal. The scheme that uses Wyner-Ziv binning shares elements with Cover and El Gamal original compress-and-forward, but differs from it mainly in that backward decoding is employed instead of forward decoding and the compression indices are not decoded uniquely. Finally, by exploring the properties of our outer bound, we show that, although not required in general, the compression indices can in fact be decoded uniquely essentially without altering the capacity region, but at the expense of larger alphabets sizes for the auxiliary random variables.

Includes members:
  Member "multiple access channels with states causally known at transmitters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative multiple access channels with distributed state information")
  Member "capacity region of multiple access channel with states known noncausally at one encoder and only strictly causally at the other encoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer gelfand pinsker strategies for the generalized multiple access channel")
  Member "a note on multiple access channels with strictly causal state information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiple access channels with states causally known at transmitters")
  Member "multiple access channel with states known noncausally at one encoder and only strictly causally at the other encoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on cooperative multiple access channels with delayed csi at transmitters")
  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")

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)