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 "simple multi party set reconciliation"):
  Abstract: As users migrate information to cloud storage, many distributed cloud-based services use multiple loosely consistent replicas of user information to avoid the high overhead of more tightly coupled synchronization. Periodically, the information must be synchronized, or reconciled. One can place this problem in the theoretical framework of {\em set reconciliation}: two parties $A_1$ and $A_2$ each hold a set of keys, named $S_1$ and $S_2$ respectively, and the goal is for both parties to obtain $S_1 \cup S_2$. Typically, set reconciliation is interesting algorithmically when sets are large but the set difference $|S_1-S_2|+|S_2-S_1|$ is small. In this setting the focus is on accomplishing reconciliation efficiently in terms of communication; ideally, the communication should depend on the size of the set difference, and not on the size of the sets. #R##N#In this paper, we extend recent approaches using Invertible Bloom Lookup Tables (IBLTs) for set reconciliation to the multi-party setting. In this setting there are three or more parties $A_1,A_2,\ldots,A_n$ holding sets of keys $S_1,S_2,\ldots,S_n$ respectively, and the goal is for all parties to obtain $\cup_i S_i$. This could of course be done by pairwise reconciliations, but we seek more effective methods. #R##N#Our methodology uses network coding techniques in conjunction with IBLTs, allowing efficiency in network utilization along with efficiency obtained by passing messages of size $O(|\cup_i S_i - \cap_i S_i|)$. Further, our approach can function even if the number of parties is not exactly known in advance, and in many cases can be used to determine which parties contain keys not in the joint union. By connecting reconciliation with network coding, we can allow for substantially more efficient reconciliation methods that apply to a number of natural distributed computing problems.

Includes members:
  Member "reconciling similar sets of data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data dissemination problem in wireless networks")
  Member "multi party set reconciliation using characteristic polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandwidth efficient transaction relay for bitcoin")
  Member "on thresholds for the appearance of 2 cores in mixed hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards optimal degree distributions for left perfect matchings in random bipartite graphs")
  Member "randomized algorithms for tracking distributed count frequencies and ranks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "invertible bloom lookup tables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")
  Member "analyzing network coding gossip made easy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synchronous transmissions in low power wireless a survey of communication protocols and network services")
  Member "fuzzy extractors how to generate strong keys from biometrics and other noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a capacity achieving one message key agreement with finite blocklength analysis")
  Member "straggler identification in round trip data streams via newton s identities and invertible bloom filters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")

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)