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 "fast diameter computation within split graphs"):
  Abstract: When can we compute the diameter of a graph in quasi linear time? We address this question for the class of split graphs, that we observe to be the hardest instances for deciding whether the diameter is at most two. We stress that although the diameter of a non-complete split graph can only be either 2 or 3, under the Strong Exponential-Time Hypothesis (SETH) we cannot compute the diameter of a split graph in less than quadratic time. Therefore it is worth to study the complexity of diameter computation on subclasses of split graphs, in order to better understand the complexity border. Specifically, we consider the split graphs with bounded clique-interval number and their complements, with the former being a natural variation of the concept of interval number for split graphs that we introduce in this paper. We first discuss the relations between the clique-interval number and other graph invariants such as the classic interval number of graphs, the treewidth, the VC-dimension and the stabbing number of a related hypergraph. Then, in part based on these above relations, we almost completely settle the complexity of diameter computation on these subclasses of split graphs: $\bullet$ For the k-clique-interval split graphs, we can compute their diameter in truly subquadratic time if k = O(1), and even in quasi linear time if k = o(log n) and in addition a corresponding ordering is given. However, under SETH this cannot be done in truly subquadratic time for any k = $\omega$(log n). $\bullet$ For the complements of k-clique-interval split graphs, we can compute their diameter in truly subquadratic time if k = O(1), and even in time O(km) if a corresponding ordering is given. Again this latter result is optimal under SETH up to polylogarithmic factors. Our findings raise the question whether a k-clique interval ordering can always be computed in quasi linear time. We prove that it is the case for k = 1 and for some subclasses such as bounded-treewidth split graphs, threshold graphs and comparability split graphs. Finally, we prove that some important subclasses of split graphs-including the ones mentioned above-have a bounded clique-interval number.

Includes members:
  Member "diameter computation on h minor free graphs and graphs of bounded distance vc dimension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a story of diameter radius and helly property")
  Member "multivariate analysis of orthogonal range searching and graph distances parameterized by treewidth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diameter computation on h minor free graphs and graphs of bounded distance vc dimension")
  Member "identifying codes in hereditary classes of graphs and vc dimension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diameter computation on h minor free graphs and graphs of bounded distance vc dimension")
  Member "into the square on the complexity of quadratic time solvable problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diameter computation on h minor free graphs and graphs of bounded distance vc dimension")

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)