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: "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.

Connected through hyperedge HE-1 (induced by source paper "bandwidth efficient transaction relay for bitcoin"):
  with members:
    - "dandelion lightweight cryptocurrency networking with formal anonymity guarantees"
    - "txprobe discovering bitcoin s network topology using orphan transactions"
    - "bitcoin ng a scalable blockchain protocol"
    - "multi party set reconciliation using characteristic polynomials"
    - "deanonymisation of clients in bitcoin p2p network"
    - "bitcoin transaction malleability and mtgox"
    - "majority is not enough bitcoin mining is vulnerable"
    - "hosting byzantine fault tolerant services on a chord ring"

Connected through hyperedge HE-2 (induced by source paper "robust set reconciliation via locality sensitive hashing"):
  with members:
    - "reconciling graphs and sets of sets"
    - "wear minimization for cuckoo hashing how not to throw a lot of eggs into one basket"
    - "parallel peeling algorithms"
    - "invertible bloom lookup tables"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-3 (induced by source paper "reconciling graphs and sets of sets"):
  with members:
    - "models and algorithms for graph watermarking"
    - "graph isomorphism in quasipolynomial time"
    - "multi party set reconciliation using characteristic polynomials"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "an efficient reconciliation algorithm for social networks"
    - "invertible bloom lookup tables"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-4 (induced by source paper "multi party set reconciliation using characteristic polynomials"):
  with members:
    - "invertible bloom lookup tables"
    - "analyzing network coding gossip made easy"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

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.DS(Data Structures and Algorithms)