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 "round complexity of common randomness generation the amortized setting"):
  Abstract: We study the effect of rounds of interaction on the common randomness generation (CRG) problem. In the CRG problem, two parties, Alice and Bob, receive samples $X_i$ and $Y_i$, respectively, drawn jointly from a source distribution $\mu$. The two parties wish to agree on a common random key consisting of many bits of randomness, by exchanging messages that depend on each party's input and the previous messages. In this work we study the amortized version of the problem, i.e., the number of bits of communication needed per random bit output by Alice and Bob, in the limit as the number of bits generated tends to infinity. The amortized version of the CRG problem has been extensively studied, though very little was known about the effect of interaction on this problem. Recently Bafna et al. (SODA 2019) considered the non-amortized version of the problem: they gave a family of sources $\mu_{r,n}$ parameterized by $r,n\in\mathbb{N}$, such that with $r+2$ rounds of communication one can generate $n$ bits of common randomness with this source with $O(r\log n)$ communication, whereas with roughly $r/2$ rounds the communication complexity is $\Omega(n/{\rm poly}\log n)$. Note that their source is designed with the target number of bits in mind and hence the result does not apply to the amortized setting. #R##N#In this work we strengthen the work of Bafna et al. in two ways: First we show that the results extend to the amortized setting. We also reduce the gap between the round complexity in the upper and lower bounds to an additive constant. Specifically we show that for every pair $r,n \in \mathbb{N}$ the (amortized) communication complexity to generate $\Omega(n)$ bits of common randomness from the source $\mu_{r,n}$ using $r+2$ rounds of communication is $O(r\log n)$ whereas the amortized communication required to generate the same amount of randomness from $r$ rounds is $\Omega(\sqrt n)$.

Includes members:
  Member "communication rounds tradeoffs for common randomness and secret key generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interactive secure function computation")
  Member "resource efficient common randomness and secret key schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication for generating correlation a unifying survey")
  Member "the power of shared randomness in uncertain communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal communication universal graphs and graph labeling")
  Member "secret key generation with limited interaction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication for generating correlation a unifying survey")
  Member "on the role of shared randomness in simultaneous communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication rounds tradeoffs for common randomness and secret key generation")
  Member "secret key generation with one communicator and a one shot converse via hypercontractivity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication for generating correlation a unifying survey")
  Member "communication complexity of permutation invariant functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication rounds tradeoffs for common randomness and secret key generation")
  Member "communication with imperfectly shared randomness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal communication universal graphs and graph labeling")

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)