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 "separate measure and conquer faster algorithms for max 2 csp and counting dominating sets"):
  Abstract: We show a method resulting in the improvement of several polynomial-space, exponential-time algorithms. An instance of the problem Max (r,2)-CSP, or simply Max 2-CSP, is parametrized by the domain size r (often 2), the number of variables n (vertices in the constraint graph G), and the number of constraints m (edges in G). When G is cubic, and omitting sub-exponential terms here for clarity, we give an algorithm running in time r (1/5) n = r (2/15) m ; the previous best was r (1/4) n = r (1/6) m . By known results, this improvement for the cubic case results in an algorithm running in time r (9/50) m for general instances; the previous best was r (19/100) m . We show that the analysis of the earlier algorithm was tight: our improvement is in the algorithm, not just the analysis. The new algorithm, like the old, extends to Polynomial and Ring CSP (encompassing graph bisection, the Ising model, and counting). It is also the fastest polynomial- space algorithm for Max Cut, for cubic and general instances. We also give faster algorithms for #Dominating Set, counting the dominating sets of every cardinality 0,...,n for a graph G of order n. For cubic graphs, our algorithm runs in time 3 (1/6) n ; the previous best was 2 (1/2) n . For general graphs, we give an unrelated algorithm running in time 1.5183 n ; the previous best was 1.5673 n . The previous best algorithms for these problems all used local transformations and were an- alyzed by the "Measure and Conquer" method. Our new algorithms capitalize on the existence of small balanced separators for cubic graphs — a non-local property — and the ability to tailor the local algorithms always to "pivot" on a vertex in the separator. The new algorithms perform much as the old ones until the separator is empty, at which point they gain because the remaining vertices are split into two independent problem instances that can be solved recursively. It is likely that such algorithms can be effective for other problems too, and we present their design and analysis in a general framework.

Includes members:
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "linear kernels and single exponential algorithms via protrusion decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hitting topological minors is fpt")
  Member "strong backdoors to bounded treewidth sat":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph width measures for cnf encodings with auxiliary variables")
  Member "polynomial constraint satisfaction problems graph bisection and the ising partition function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "h colouring p_t free graphs in subexponential time")
  Member "a universally fastest algorithm for max 2 sat max 2 csp and everything in between":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward super polynomial size lower bounds for depth two threshold circuits")

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)