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: "separate chaining meets compact hashing"
  Abstract: While separate chaining is a common strategy for resolving collisions in a hash table taught in most textbooks, compact hashing is a less common technique for saving space when hashing integers whose domain is relatively small with respect to the problem size. It is widely believed that hash tables waste a considerable amount of memory, as they either leave allocated space untouched (open addressing) or store additional pointers (separate chaining). For the former, Cleary introduced the compact hashing technique that stores only a part of a key to save space. However, as can be seen by the line of research focusing on compact hash tables with open addressing, there is additional information, called displacement, required for restoring a key. There are several representations of this displacement information with different space and time trade-offs. In this article, we introduce a separate chaining hash table that applies the compact hashing technique without the need for the displacement information. Practical evaluations reveal that insertions in this hash table are faster or use less space than all previously known compact hash tables on modern computer architectures when storing sufficiently large satellite data.

Connected through hyperedge HE-1 (induced by source paper "dynamic path decomposed tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "m bonsai a practical compact dynamic trie"
    - "packed compact tries a fast and efficient data structure for online string processing"
    - "a framework of dynamic data structures for string processing"
    - "practical evaluation of lempel ziv 78 and lempel ziv welch tries"
    - "from theory to practice plug and play with succinct data structures"
    - "fast compressed tries through path decompositions"

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)