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: "fast counting with tensor networks"
  Abstract: We introduce tensor network contraction algorithms for the counting of satisfying assignments in constraint satisfaction problems (\#CSP). We represent each arbitrary \#CSP instance as a tensor network whose full contraction yields the number of satisfying assignments for that instance. We then use methods of graph theory and computational complexity theory to develop fast tensor contraction protocols. In particular, we employ the tools of multilevel graph partitioning and community structure detection to determine favorable orders of contraction of arbitrary tensor networks. We first prove analytically that full tensor contraction can be performed in subexponential time for \#CSP instances defined on graphs with sublinear separators. We then implement numerical heuristics for the solution of general \#P-hard counting boolean satisfiability (\#SAT) problems, focusing on random instances of \#\textsc{Cubic-Vertex-Cover} as a concrete example, and show that they outperform state-of-the-art \#SAT solvers by a significant margin. Our results promote tensor network contraction algorithms as a powerful practical tool for fast solution of some \#CSPs.

Connected through hyperedge HE-1 (induced by source paper "efficient contraction of large tensor networks for weighted model counting through graph decompositions"):
  with members:
    - "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"
    - "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"

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)