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 "sending a message with unknown noise"):
  Abstract: Alice and Bob are connected via a two-way channel, and Alice wants to send a message of $L$ bits to Bob. An adversary flips an arbitrary but finite number of bits, $T$, on the channel. This adversary knows our algorithm and Alice's message, but does not know any private random bits generated by Alice or Bob, nor the bits sent over the channel, except when these bits can be predicted by knowledge of Alice's message or our algorithm. We want Bob to receive Alice's message and for both players to terminate, with error probability at most $\delta > 0$, where $\delta$ is a parameter known to both Alice and Bob. Unfortunately, the value $T$ is unknown in advance to either Alice or Bob, and the value $L$ is unknown in advance to Bob. We describe an algorithm to solve the above problem while sending an expected $L + O \left( T + \min \left(T+1,\frac{L}{\log L} \right) \log \left( \frac{L}{\delta} \right) \right)$ bits. A special case is when $\delta = O(1/L^c)$, for some constant $c$. Then when $T = o(L/\log L)$, the expected number of bits sent is $L + o(L)$, and when $T = \Omega(L)$, the expected number of bits sent is $L + O\left( T \right)$, which is asymptotically optimal.

Includes members:
  Member "distributed computing with channel noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")
  Member "interactive communication with unknown noise rate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")
  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 "optimal error rates for interactive coding ii efficiency and list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")
  Member "tree codes and a conjecture on exponential sums":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on decoding cohen haeupler schulman tree codes")
  Member "physical layer network coding tutorial survey and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")

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)