human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "solving and sampling with many solutions satisfiability and other hard problems":
    also connected through other hyperedges:
      (none)
  Member "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning")
  Member "implementing efficient all solutions sat solvers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting database management systems and treewidth for counting")
  Member "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "approximation algorithms for polynomial expansion and low density graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diameter computation on h minor free graphs and graphs of bounded distance vc dimension")
  Member "aslib a benchmark library for algorithm selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extreme algorithm selection with dyadic feature representation")
  Member "recent advances in graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "satzilla portfolio based algorithm selection for sat":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")

Question: Please tell me which class the source paper 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)