human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "lossy transmission of correlated sources over two way channels"
  Abstract: Achievability and converse results for the lossy transmission of correlated sources over Shannon's two-way channels (TWCs) are presented. A joint source-channel coding theorem for independent sources and TWCs for which adaptation cannot enlarge the capacity region is also established. We further investigate the optimality of scalar coding for TWCs with discrete modulo additive noise as well as additive white Gaussian noise. Comparing the distortion of scalar coding with the derived bounds, we observe that scalar coding achieves the minimum distortion over both families of TWCs for independent and uniformly distributed sources and independent Gaussian sources.

Connected through hyperedge HE-1 (induced by source paper "adaptive coding for two way lossy source channel communication"):
  with members:
    - "joint source channel coding for the transmission of correlated sources over two way channels"
    - "two way source coding with a helper"

Connected through hyperedge HE-2 (induced by source paper "joint source channel coding for the transmission of correlated sources over two way channels"):
  with members:
    - "adaptation is useless for two discrete additive noise two way channels"
    - "two way networks when adaptation is useless"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-3 (induced by source paper "scalar quantizer design for two way channels"):
  with members:
    - "joint source channel coding for the transmission of correlated sources over two way channels"
    - "scalar quantize and forward for symmetric half duplex two way relay channels"

Connected through hyperedge HE-4 (induced by source paper "capacity of generalized discrete memoryless push to talk two way channels"):
  with members:
    - "adaptation is useless for two discrete additive noise two way channels"
    - "two way networks when adaptation is useless"

Question: Please tell me which class the center node 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)