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: "fine grained reductions from approximate counting to decision"
  Abstract: In this paper, we introduce a general framework for fine-grained reductions of approximate counting problems to their decision versions. (Thus we use an oracle that decides whether any witness exists to multiplicatively approximate the number of witnesses with minimal overhead.) This mirrors a foundational result of Sipser (STOC 1983) and Stockmeyer (SICOMP 1985) in the polynomial-time setting, and a similar result of Muller (IWPEC 2006) in the FPT setting. Using our framework, we obtain such reductions for some of the most important problems in fine-grained complexity: the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path). While all these problems have simple algorithms over which it is conjectured that no polynomial improvement is possible, our reductions would remain interesting even if these conjectures were proved; they have only polylogarithmic overhead, and can therefore be applied to subpolynomial improvements such as the n3/exp(Θ(√logn))-time algorithm for the Negative-Weight Triangle problem due to Williams (STOC 2014). Our framework is also general enough to apply to versions of the problems for which more efficient algorithms are known. For example, the Orthogonal Vectors problem over GF(m)d for constant m can be solved in time n·poly(d) by a result of Williams and Yu (SODA 2014); our result implies that we can approximately count the number of orthogonal pairs with essentially the same running time. We also provide a fine-grained reduction from approximate #SAT to SAT. Suppose the Strong Exponential Time Hypothesis (SETH) is false, so that for some 1  0 as part of the input). A full version of this paper containing detailed proofs is available at https://arxiv.org/abs/1707.04609.

Connected through hyperedge HE-1 (induced by source paper "on the fine grained complexity of parity problems"):
  with members:
    - "subcubic equivalences between graph centrality measures and complementary problems"
    - "approximating subset sum is equivalent to min plus convolution"
    - "on problems equivalent to min convolution"
    - "a subquadratic algorithm for 3xor"
    - "fine grained i o complexity via reductions new lower bounds faster algorithms and a time hierarchy"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "tight hardness results for maximum weight rectangles"
    - "on problems as hard as cnf sat"

Connected through hyperedge HE-2 (induced by source paper "approximately counting and sampling small witnesses using a colourful decision oracle"):
  with members:
    - "counting answers to existential questions"
    - "zeros of holant problems locations and algorithms"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "near optimal linear decision trees for k sum and related problems"
    - "edge estimation with independent set oracles"
    - "the hardness of embedding grids and walls"
    - "randomised enumeration of small witnesses using a decision oracle"
    - "the challenges of unbounded treewidth in parameterised subgraph counting problems"

Connected through hyperedge HE-3 (induced by source paper "inner product oracle can estimate and sample"):
  with members:
    - "sublinear time numerical linear algebra for structured matrices"
    - "testing matrix rank optimally"
    - "edge estimation with independent set oracles"
    - "on sampling edges almost uniformly"
    - "computing exact minimum cuts without knowing the graph"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"

Connected through hyperedge HE-4 (induced by source paper "hyperedge estimation using polylogarithmic subset queries"):
  with members:
    - "parameterized query complexity of hitting set using stability of sunflowers"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "computing exact minimum cuts without knowing the graph"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "finding cycles and trees in sublinear time"

Connected through hyperedge HE-5 (induced by source paper "edge estimation with independent set oracles"):
  with members:
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "shortest paths in intersection graphs of unit disks"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

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)