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 "interactive communication with unknown noise rate"):
  Abstract: Alice and Bob want to run a protocol over a noisy channel, where a certain number of bits are flipped adversarially. Several results take a protocol requiring $L$ bits of noise-free communication and make it robust over such a channel. In a recent breakthrough result, Haeupler described an algorithm that sends a number of bits that is conjectured to be near optimal in such a model. However, his algorithm critically requires $a \ priori$ knowledge of the number of bits that will be flipped by the adversary. #R##N#We describe an algorithm requiring no such knowledge. If an adversary flips $T$ bits, our algorithm sends $L + O\left(\sqrt{L(T+1)\log L} + T\right)$ bits in expectation and succeeds with high probability in $L$. It does so without any $a \ priori$ knowledge of $T$. Assuming a conjectured lower bound by Haeupler, our result is optimal up to logarithmic factors. #R##N#Our algorithm critically relies on the assumption of a private channel. We show that privacy is necessary when the amount of noise is unknown.

Includes members:
  Member "interactive channel capacity revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")
  Member "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")
  Member "competitive mac under adversarial sinr":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic digital clustering of wireless ad hoc networks")
  Member "optimal error rates for interactive coding i adaptivity and other settings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interactive coding resilient to an unknown number of erasures")
  Member "tree codes and a conjecture on exponential sums":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on decoding cohen haeupler schulman tree codes")
  Member "making evildoers pay resource competitive broadcast in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")

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.DS(Data Structures and Algorithms)