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 "on the duality of additivity and tensorization"):
  Abstract: A function is said to be additive if, similar to mutual information, expands by a factor of $n$, when evaluated on $n$ i.i.d. repetitions of a source or channel. On the other hand, a function is said to satisfy the tensorization property if it remains unchanged when evaluated on i.i.d. repetitions. Additive rate regions are of fundamental importance in network information theory, serving as capacity regions or upper bounds thereof. Tensorizing measures of correlation have also found applications in distributed source and channel coding problems as well as the distribution simulation problem. Prior to our work only two measures of correlation, namely the hypercontractivity ribbon and maximal correlation (and their derivatives), were known to have the tensorization property. In this paper, we provide a general framework to obtain a region with the tensorization property from any additive rate region. We observe that hypercontractivity ribbon indeed comes from the dual of the rate region of the Gray-Wyner source coding problem, and generalize it to the multipartite case. Then we define other measures of correlation with similar properties from other source coding problems. We also present some applications of our results.

Includes members:
  Member "on non interactive simulation of joint distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey")
  Member "key capacity with limited one way communication for product sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accuracy memory tradeoffs and phase transitions in belief propagation")
  Member "an exploration of the role of principal inertia components in information theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the courtade kumar conjecture for certain classes of boolean functions")
  Member "key capacity for product sources with application to stationary gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication for generating correlation a unifying survey")
  Member "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning")
  Member "a new data processing inequality and its applications in distributed source and channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimation efficiency under privacy constraints")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the direction of discrimination an information theoretic analysis of disparate impact in machine 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)