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 "encoding two dimensional range top k queries revisited"):
  Abstract: We consider the problem of encoding two-dimensional arrays, whose elements come from a total order, for answering top-k queries. The aim is to obtain encodings that use space close to the information-theoretic lower bound, which can be constructed efficiently. For $2 \times n$ arrays, we first give upper and lower bounds on space for answering sorted and unsorted 3-sided top-k queries. For $m \times n$ arrays, with $m \le n$ and $k \le mn$, we obtain $(m\lg{{(k+1)n \choose n}}+4nm(m-1)+o(n))$-bit encoding for answering sorted 4-sided top-k queries. This improves the $\min{(O(mn\lg{n}),m^2\lg{{(k+1)n \choose n}} +m\lg{m}+o(n))}$-bit encoding of Jo et al. [CPM, 2016] when $m = o(\lg{n})$. This is a consequence of a new encoding that encodes a $2 \times n$ array to support sorted 4-sided top-k queries on it using an additional $4n$ bits, in addition to the encodings to support the top-k queries on individual rows. This new encoding is a non-trivial generalization of the encoding of Jo et al. [CPM, 2016] that supports sorted 4-sided top-2 queries on it using an additional $3n$ bits. We also give almost optimal space encodings for $3$-sided top-k queries, and show lower bounds on encodings for $3$-sided and $4$-sided top-k queries.

Includes members:
  Member "encodings of range maximum sum segment queries and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal encodings for range top k selection and min max")
  Member "optimal encodings for range top k selection and min max":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the effective entropy of next previous larger smaller value queries")

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)