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 "interesting paths in the mapper"):
  Abstract: The Mapper produces a compact summary of high dimensional data as a simplicial complex. We study the problem of quantifying the interestingness of subpopulations in a Mapper, which appear as long paths, flares, or loops. First, we create a weighted directed graph G using the 1-skeleton of the Mapper. We use the average values at the vertices of a target function to direct edges (from low to high). The difference between the average values at vertices (high-low) is set as its weight. Covariation of the remaining h functions (independent variables) is captured by a h-bit binary signature assigned to the edge. An interesting path in G is a directed path whose edges all have the same signature. We define the interestingness score of such a path as a sum of its edge weights multiplied by a nonlinear function of their ranks in the path. #R##N#Second, we study three optimization problems on this graph G. In the problem Max-IP, we seek an interesting path in G with the maximum interestingness score. We show that Max-IP is NP-complete. For the special case when G is a directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G. #R##N#In the more general problem IP, the goal is to find a collection of interesting paths such that these paths form an exact cover of E (hence they are edge-disjoint) and the overall sum of interestingness scores of all paths is maximized. We also study a variant of IP termed k-IP, where the goal is to identify a collection of edge-disjoint interesting paths each with k edges, and the total interestingness score of all paths is maximized. While k-IP can be solved in polynomial time for k  = 3. Further, we show that k-IP remains NP-complete for k >= 3 even for the case when G is a DAG. We develop polynomial time heuristics for IP and k-IP on DAGs.

Includes members:
  Member "statistical analysis and parameter selection for mapper":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph mapper seeing graphs through the neural lens")
  Member "a data structure for nearest common ancestors with linking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data structures for weighted matching and extensions to b matching and f factors")
  Member "mutiscale mapper a framework for topological summarization of data and maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jaccard filtration and stable paths in the mapper")

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.CG(Computational Geometry)