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 "distributed computing with channel noise"):
  Abstract: A group of $n$ users want to run a distributed protocol $\pi$ over a network where communication occurs via private point-to-point channels. Unfortunately, an adversary, who knows $\pi$, is able to maliciously flip bits on the channels. Can we efficiently simulate $\pi$ in the presence of such an adversary? We show that this is possible, even when $L$, the number of bits sent in $\pi$, and $T$, the number of bits flipped by the adversary are not known in advance. In particular, we show how to create a robust version of $\pi$ that 1) fails with probability at most $\delta$, for any $\delta>0$; and 2) sends $\tilde{O}(L + T)$ bits, where the $\tilde{O}$ notation hides a $\log (nL/ \delta)$ term multiplying $L$. Additionally, we show how to improve this result when the average message size $\alpha$ is not constant. In particular, we give an algorithm that sends $O( L (1 + (1/\alpha) \log (n L/\delta) + T)$ bits. This algorithm is adaptive in that it does not require a priori knowledge of $\alpha$. We note that if $\alpha$ is $\Omega\left( \log (n L/\delta) \right)$, then this improved algorithm sends only $O(L+T)$ bits, and is therefore within a constant factor of optimal.

Includes members:
  Member "interactive communication with unknown noise rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "interactive channel capacity revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")

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.CR(Cryptography and Security)