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: "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.

Connected through hyperedge HE-1 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions"):
  with members:
    - "synchronization strings channel simulations and interactive coding for insertions and deletions"
    - "coding for interactive communication correcting insertions and deletions"
    - "interactive channel capacity revisited"
    - "the adversarial noise threshold for distributed protocols"
    - "adaptive protocols for interactive communication"
    - "optimal error rates for interactive coding ii efficiency and list decoding"

Connected through hyperedge HE-2 (induced by source paper "consensus needs broadcast in noiseless models but can be exponentially easier in the presence of noise"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "tight analysis for the 3 majority consensus dynamics"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "breaking the log n barrier on rumor spreading"
    - "stabilizing consensus with many opinions"
    - "noisy rumor spreading and plurality consensus"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-3 (induced by source paper "interactive coding resilient to an unknown number of erasures"):
  with members:
    - "sending a message with unknown noise"
    - "interactive communication with unknown noise rate"
    - "coding for interactive communication correcting insertions and deletions"
    - "maximal noise in interactive communication over erasure channels and channels with feedback"
    - "interactive channel capacity revisited"
    - "adaptive protocols for interactive communication"
    - "optimal error rates for interactive coding i adaptivity and other settings"
    - "optimal error rates for interactive coding ii efficiency and list decoding"

Connected through hyperedge HE-4 (induced by source paper "sending a message with unknown noise"):
  with members:
    - "interactive communication with unknown noise rate"
    - "interactive channel capacity revisited"
    - "optimal error rates for interactive coding i adaptivity and other settings"
    - "optimal error rates for interactive coding ii efficiency and list decoding"
    - "tree codes and a conjecture on exponential sums"
    - "physical layer network coding tutorial survey and beyond"

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.CR(Cryptography and Security)