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 "worst case optimal join algorithms techniques results and open problems"):
  Abstract: Worst-case optimal join algorithms are the class of join algorithms whose runtime match the worst-case output size of a given join query. While the first provably worse-case optimal join algorithm was discovered relatively recently, the techniques and results surrounding these algorithms grow out of decades of research from a wide range of areas, intimately connecting graph theory, algorithms, information theory, constraint satisfaction, database theory, and geometric inequalities. These ideas are not just paperware: in addition to academic project implementations, two variations of such algorithms are the work-horse join algorithms of commercial database and data analytics engines. #R##N#This paper aims to be a brief introduction to the design and analysis of worst-case optimal join algorithms. We discuss the key techniques for proving runtime and output size bounds. We particularly focus on the fascinating connection between join algorithms and information theoretic inequalities, and the idea of how one can turn a proof into an algorithm. Finally, we conclude with a representative list of fundamental open problems in this area.

Includes members:
  Member "ac dc in database learning thunderstruck":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows":
    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 "complexity analysis of general and fractional hypertree decompositions")
  Member "constraint solving via fractional edge covers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "in database learning with sparse tensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spores sum product optimization via relational equality saturation for large scale linear algebra")
  Member "juggling functions inside a database":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rk means fast clustering for relational data")
  Member "worst case optimal algorithms for parallel query processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins")
  Member "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constant delay enumeration with fpt preprocessing for conjunctive queries of bounded submodular width")

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.DB(Databases)