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: "on thresholds for the appearance of 2 cores in mixed hypergraphs"
  Abstract: We study thresholds for the appearance of a 2-core in random hypergraphs that are a mixture of a constant number of random uniform hypergraphs each with a linear number of edges but with different edge sizes. For the case of two overlapping hypergraphs we give a solution for the optimal (expected) number of edges of each size such that the 2-core threshold for the resulting mixed hypergraph is maximized. We show that for adequate edge sizes this threshold exceeds the maximum 2-core threshold for any random uniform hypergraph, which can be used to improve the space utilization of several data structures that rely on this parameter.

Connected through hyperedge HE-1 (induced by source paper "simple multi party set reconciliation"):
  with members:
    - "reconciling similar sets of data"
    - "multi party set reconciliation using characteristic polynomials"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "towards optimal degree distributions for left perfect matchings in random bipartite graphs"):
  with members:
    - "sharp load thresholds for cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "maximum matchings in random bipartite graphs and the space utilization of cuckoo hashtables"

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)