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 "complexity analysis of general and fractional hypertree decompositions"):
  Abstract: Hypertree decompositions (HDs), as well as the more powerful generalized hypertree decompositions (GHDs), and the yet more general fractional hypertree decompositions (FHDs) are hypergraph decomposition methods successfully used for answering conjunctive queries and for solving constraint satisfaction problems. Every hypergraph $H$ has a width relative to each of these methods: its hypertree width $hw(H)$, its generalized hypertree width $ghw(H)$, and its fractional hypertree width $fhw(H)$, respectively. It is known that $hw(H)\leq k$ can be checked in polynomial time for fixed $k$, while checking $ghw(H)\leq k$ is NP-complete for $k \geq 3$. The complexity of checking $fhw(H)\leq k$ for a fixed $k$ has been open for over a decade. #R##N#We settle this open problem by showing that checking $fhw(H)\leq k$ is NP-complete, even for $k=2$. The same construction allows us to prove also the NP-completeness of checking $ghw(H)\leq k$ for $k=2$. After that, we identify meaningful restrictions which make checking for bounded $ghw$ or $fhw$ tractable or allow for an efficient approximation of the $fhw$.

Includes members:
  Member "hyperbench a benchmark and tool for hypergraphs and empirical findings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "potential maximal cliques parameterized by edge clique cover")
  Member "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms":
    also connected through other hyperedges:
      (none)
  Member "size bounds and query plans for relational joins":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast join project query evaluation using matrix multiplication")
  Member "constraint solving via fractional edge covers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constant delay enumeration with fpt preprocessing for conjunctive queries of bounded submodular width")
  Member "an analytical study of large sparql query logs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge graphs")
  Member "old techniques for new join algorithms a case study in rdf processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperbench a benchmark and tool for hypergraphs and empirical findings")
  Member "general and fractional hypertree decompositions hard and easy cases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "potential maximal cliques parameterized by edge clique cover")
  Member "myhill nerode methods for hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the art of trellis decoding is fixed parameter tractable")

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.CC(Computational Complexity)