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: "on problems as hard as cnf sat"
  Abstract: The field of exact exponential time algorithms for non-deterministic polynomial-time hard problems has thrived since the mid-2000s. While exhaustive search remains asymptotically the fastest known algorithm for some basic problems, non-trivial exponential time algorithms have been found for a myriad of problems, including G raph  C oloring , H amiltonian  P ath , D ominating  S et , and 3-CNF-S at . In some instances, improving these algorithms further seems to be out of reach. The CNF-S at  problem is the canonical example of a problem for which the trivial exhaustive search algorithm runs in time O(2n), where n is the number of variables in the input formula. While there exist non-trivial algorithms for CNF-S at  that run in time o(2n), no algorithm was able to improve the growth rate 2 to a smaller constant, and hence it is natural to conjecture that 2 is the optimal growth rate. The strong exponential time hypothesis (SETH) by Impagliazzo and Paturi [JCSS 2001] goes a little bit further and asserts that, for every e  at  cannot be computed in time 2en.   In this article, we show that, for every e  itting  S et , S et  S plitting , and NAE-S at  cannot be computed in time O(2en) unless SETH fails. Here n is the number of elements or variables in the input. For these problems, we actually get an equivalence to SETH in a certain sense. We conjecture that SETH implies a similar statement for S et  C over  and prove that, under this assumption, the fastest known algorithms for S teiner  T ree , C onnected  V ertex  C over , S et  P artitioning , and the pseudo-polynomial time algorithm for S ubset  S um  cannot be significantly improved. Finally, we justify our assumption about the hardness of S et  C over  by showing that the parity of the number of solutions to S et  C over  cannot be computed in time O(2en) for any e

Connected through hyperedge HE-1 (induced by source paper "coarse grained complexity for dynamic algorithms"):
  with members:
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "improved algorithms for fully dynamic maximal independent set"
    - "expander decomposition and pruning faster stronger and simpler"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic algorithms for graph coloring"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"

Connected through hyperedge HE-2 (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 reductions from approximate counting to decision"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "scheduling lower bounds via and subset sum"):
  with members:
    - "conditional hardness of earth mover distance"
    - "polyline simplification has cubic complexity"
    - "a simple near linear pseudopolynomial time randomized algorithm for subset sum"
    - "fast modular subset sum using linear sketching"
    - "algorithms and hardness for diameter in dynamic graphs"
    - "seth based lower bounds for subset sum and bicriteria path"
    - "fine grained i o complexity via reductions new lower bounds faster algorithms and a time hierarchy"
    - "a near linear pseudopolynomial time algorithm for subset sum"

Connected through hyperedge HE-4 (induced by source paper "structural parameterizations with modulator oblivion"):
  with members:
    - "polylogarithmic approximation algorithms for weighted mathcal f deletion problems"
    - "0 1 all csps half integral a path packing and linear time fpt algorithms"
    - "approximation and kernelization for chordal vertex deletion"
    - "large induced subgraphs via triangulations and cmso"
    - "faster parameterized algorithms using linear programming"
    - "on multiway cut parameterized above lower bounds"

Connected through hyperedge HE-5 (induced by source paper "on problems equivalent to min convolution"):
  with members:
    - "how hard is it to find honest witnesses"
    - "seth based lower bounds for subset sum and bicriteria path"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "clustered integer 3sum via additive combinatorics"
    - "a faster fptas for the unbounded knapsack problem"
    - "a faster pseudopolynomial time algorithm for subset sum"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"

Connected through hyperedge HE-6 (induced by source paper "equal subset sum faster than the meet in the middle"):
  with members:
    - "ppp completeness with connections to cryptography"
    - "a simple near linear pseudopolynomial time randomized algorithm for subset sum"
    - "subset sum made simple"
    - "seth based lower bounds for subset sum and bicriteria path"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "a faster pseudopolynomial time algorithm for subset sum"
    - "space time tradeoffs for subset sum an improved worst case algorithm"

Connected through hyperedge HE-7 (induced by source paper "detecting feedback vertex sets of size k in o star 2 7 k time"):
  with members:
    - "losing treewidth by separating subsets"
    - "a naive algorithm for feedback vertex set"
    - "fine grained parameterized complexity analysis of graph coloring problems"
    - "exact algorithms via monotone local search"
    - "on feedback vertex set new measure and new structures"
    - "powers of tensors and fast matrix multiplication"
    - "faster deterministic feedback vertex set"
    - "solving connectivity problems parameterized by treewidth in single exponential time"

Connected through hyperedge HE-8 (induced by source paper "lower bounds for the happy coloring problems"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "algorithms and hardness results for happy coloring problems"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "exponential time complexity of the permanent and the tutte polynomial"
    - "kernelization of packing problems"
    - "quasirandom rumor spreading"

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)