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: "benchmarking treewidth as a practical component of tensor network simulations"
  Abstract: Tensor networks are powerful factorization techniques which reduce resource requirements for numerically simulating principal quantum many-body systems and algorithms. The computational complexity of a tensor network simulation depends on the tensor ranks and the order in which they are contracted. Unfortunately, computing optimal contraction sequences (orderings) in general is known to be a computationally difficult (NP-complete) task. In 2005, Markov and Shi showed that optimal contraction sequences correspond to optimal (minimum width) tree decompositions of a tensor network's line graph, relating the contraction sequence problem to a rich literature in structural graph theory. While treewidth-based methods have largely been ignored in favor of dataset-specific algorithms in the prior tensor networks literature, we demonstrate their practical relevance for problems arising from two distinct methods used in quantum simulation: multi-scale entanglement renormalization ansatz (MERA) datasets and quantum circuits generated by the quantum approximate optimization algorithm (QAOA). We exhibit multiple regimes where treewidth-based algorithms outperform domain-specific algorithms, while demonstrating that the optimal choice of algorithm has a complex dependence on the network density, expected contraction complexity, and user run time requirements. We further provide an open source software framework designed with an emphasis on accessibility and extendability, enabling replicable experimental evaluations and future exploration of competing methods by practitioners.

Connected through hyperedge HE-1 (induced by source paper "efficient contraction of large tensor networks for weighted model counting through graph decompositions"):
  with members:
    - "fast counting with tensor networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "answer set solving with bounded treewidth revisited"
    - "positive instance driven dynamic programming for treewidth"
    - "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-2 (induced by source paper "space efficient vertex separators for treewidth"):
  with members:
    - "space efficient dfs and applications simpler leaner faster"
    - "small uncolored and colored choice dictionaries"
    - "nearly optimal space efficient algorithm for depth first search"
    - "in place initializable arrays"
    - "rank select indices without tears"
    - "on the fly array initialization in less space"
    - "succinct choice dictionaries"

Connected through hyperedge HE-3 (induced by source paper "carving width and contraction trees for tensor networks"):
  with members:
    - "simulation of quantum many body systems on amazon cloud"
    - "on the satisfiability of quantum circuits of small treewidth"
    - "designing multi commodity flow trees"

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)