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: "positive instance driven dynamic programming for treewidth"
  Abstract: Consider a dynamic programming scheme for a decision problem in which all subproblems involved are also decision problems. An implementation of such a scheme is {\em positive-instance driven} (PID), if it generates positive subproblem instances, but not negative ones, building each on smaller positive instances. #R##N#We take the dynamic programming scheme due to Bouchitt\'{e} and Todinca for treewidth computation, which is based on minimal separators and potential maximal cliques, and design a variant (for the decision version of the problem) with a natural PID implementation. The resulting algorithm performs extremely well: it solves a number of standard benchmark instances for which the optimal solutions have not previously been known. Incorporating a new heuristic algorithm for detecting safe separators, it also solves all of the 100 public instances posed by the exact treewidth track in PACE 2017, a competition on algorithm implementation. #R##N#We describe the algorithm, prove its correctness, and give a running time bound in terms of the number of positive subproblem instances. We perform an experimental analysis which supports the practical importance of such a bound.

Connected through hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice"):
  with members:
    - "engineering kernelization for maximum cut"
    - "shared memory branch and reduce for multiterminal cuts"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional policy for solving tree decomposition via reinforcement learning heuristics"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discriminative embeddings of latent variable models for structured data"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-3 (induced by source paper "potential maximal cliques parameterized by edge clique cover"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "constraint solving via fractional edge covers"
    - "on the tractability of optimization problems on h graphs"
    - "ranked enumeration of minimal triangulations"
    - "on distance d independent set and other problems in graphs with few minimal separators"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "on the number of minimal separators in graphs"
    - "algorithms parameterized by vertex cover and modular width through potential maximal cliques"

Connected through hyperedge HE-4 (induced by source paper "efficient contraction of large tensor networks for weighted model counting through graph decompositions"):
  with members:
    - "fast counting with tensor networks"
    - "benchmarking treewidth as a practical component of tensor network simulations"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "answer set solving with bounded treewidth revisited"
    - "faq questions asked frequently"
    - "graph bisection with pareto optimization"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-5 (induced by source paper "positive instance driven dynamic programming for graph searching"):
  with members:
    - "a parameterized strongly polynomial algorithm for block structured integer programs"
    - "faster algorithms for integer programs with block structure"
    - "practical access to dynamic programming on tree decompositions"
    - "the complexity landscape of decompositional parameters for ilp"
    - "small resolution proofs for qbf using dependency treewidth"
    - "a differential approach to inference in bayesian networks"
    - "courcelle s theorem a game theoretic approach"

Connected through hyperedge HE-6 (induced by source paper "efficient implementation of color coding algorithm for subgraph isomorphism problem"):
  with members:
    - "shared memory parallel subgraph enumeration"
    - "snap a general purpose network analysis and graph mining library"

Connected through hyperedge HE-7 (induced by source paper "a heuristic use of dynamic programming to upperbound treewidth"):
  with members:
    - "a complete anytime algorithm for treewidth"
    - "treewidth computation and extremal combinatorics"

Connected through hyperedge HE-8 (induced by source paper "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of kernelization algorithms to dominating set"
    - "polynomial bounds for centered colorings on proper minor closed graph classes"
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "distributed domination on graph classes of bounded expansion"
    - "reconfiguration on nowhere dense graph classes"
    - "experimental evaluation of counting subgraph isomorphisms in classes of bounded expansion"
    - "on the number of types in sparse graphs"

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)