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: "on cooperation in multi terminal computation and rate distortion"
  Abstract: A receiver wants to compute a function of two correlated sources separately observed by two transmitters. One of the transmitters may send a possibly private message to the other transmitter in a cooperation phase before both transmitters communicate to the receiver. For this network configuration this paper investigates both a function computation setup, wherein the receiver wants to compute a given function of the sources exactly, and a rate distortion setup, wherein the receiver wants to compute a given function within some distortion. #R##N#For the function computation setup, a general inner bound to the rate region is established and shown to be tight in a number of cases: partially invertible functions, full cooperation between transmitters, one-round point-to-point communication, two-round point-to-point communication, and the cascade setup where the transmitters and the receiver are aligned. In particular it is shown that the ratio of the total number of transmitted bits without cooperation and the total number of transmitted bits with cooperation can be arbitrarily large. Furthermore, one bit of cooperation suffices to arbitrarily reduce the amount of information both transmitters need to convey to the receiver. #R##N#For the rate distortion version, an inner bound to the rate region is exhibited which always includes, and sometimes strictly, the convex hull of Kaspi-Berger's related inner bounds. The strict inclusion is shown via two examples.

Connected through hyperedge HE-1 (induced by source paper "rate distortion for lossy in network function computation information dissipation and sequential reverse water filling"):
  with members:
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "computing linear transformations with unreliable components"
    - "a generalized cut set bound for deterministic multi flow networks and its applications"
    - "dissipation of information in channels with input constraints"
    - "a generalized typicality for abstract alphabets"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"

Connected through hyperedge HE-2 (induced by source paper "intersensor collaboration in distributed quantization networks"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "distributed functional scalar quantization simplified"
    - "fixed length lossy compression in the finite blocklength regime"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "the dispersion of lossy source coding"
    - "perfect omniscience perfect secrecy and steiner tree packing"

Connected through hyperedge HE-3 (induced by source paper "distributed function computation over a rooted directed tree"):
  with members:
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "linear codes target function classes and network computing capacity"
    - "cascade multiterminal source coding"
    - "information theoretic bounds for multiround function computation in collocated networks"

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)