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: "a cuckoo hashing variant with improved memory utilization and insertion time"
  Abstract: Cuckoo hashing [4] is a multiple choice hashing scheme in which each item can be placed in multiple locations, and collisions are resolved by moving items to their alternative locations. In the classical implementation of two-way cuckoo hashing, the memory is partitioned into contiguous disjoint fixed-size buckets. Each item is hashed to two buckets, and may be stored in any of the positions within those buckets. Ref. [2] analyzed a variation in which the buckets are contiguous and overlap. However, many systems retrieve data from secondary storage in same-size blocks called pages. Fetching a page is a relatively expensive process; but once a page is fetched, its contents can be accessed orders of magnitude faster. We utilize this property of memory retrieval, presenting a variant of cuckoo hashing incorporating the following constraint: each bucket must be fully contained in a single page, but buckets are not necessarily contiguous. Empirical results show that this modification increases memory utilization and decreases the number of iterations required to insert an item. If each item is hashed to two buckets of capacity two, the page size is 8, and each bucket is fully contained in a single page, the memory utilization equals 89.71% in the classical contiguous disjoint bucket variant, 93.78% in the contiguous overlapping bucket variant, and increases to 97.46% in our new non-contiguous bucket variant. When the memory utilization is 92% and we use breadth first search to look for a vacant position, the number of iterations required to insert a new item is dramatically reduced from 545 in the contiguous overlapping buckets variant to 52 in our new non-contiguous bucket variant. In addition to the empirical results, we present a theoretical lower bound on the memory utilization of our variation as a function of the page size.

Connected through hyperedge HE-1 (induced by source paper "load thresholds for cuckoo hashing with overlapping blocks"):
  with members:
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "sharp load thresholds for cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"

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)