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 "massively parallel algorithms for string matching with wildcards"):
  Abstract: We study distributed algorithms for string matching problem in presence of wildcard characters. Given a string T (a text), we look for all occurrences of another string P (a pattern) as a substring of string T . Each wildcard character in the pattern matches a specific class of strings based on its type. String matching is one of the most fundamental problems in computer science, especially in the fields of bioinformatics and machine learning. Persistent effort has led to a variety of algorithms for the problem since 1960s. #R##N#With rise of big data and the inevitable demand to solve problems on huge data sets, there have been many attempts to adapt classic algorithms into the MPC framework to obtain further efficiency. MPC is a recent framework for parallel computation of big data, which is designed to capture the MapReduce-like algorithms. In this paper, we study the string matching problem using a set of tools translated to MPC model. We consider three types of wildcards in string matching: #R##N#- '?' wildcard: In this setting, the pattern is allowed to contain special '?' characters or don't cares that match any character of the text. String matching with don't cares could be solved by fast convolutions, and we give a constant round MPC algorithm for which by utilizing FFT in a constant number of MPC rounds. #R##N#- '+' wildcard: '+' wildcard is a special character that allows for arbitrary repetitions of a character. When the pattern contains '+' wildcard characters, our algorithm runs in a constant number of MPC rounds by a reduction from subset matching problem. #R##N#- '*' wildcard: '*' is a special character that matches with any substring of the text. When '*' is allowed in the pattern, we solve two special cases of the problem in logarithmic rounds.

Includes members:
  Member "fast algorithms for knapsack via convolution and prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating subset sum is equivalent to min plus convolution")
  Member "round compression for parallel matching algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "clustered integer 3sum via additive combinatorics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "all non trivial variants of 3 ldt are equivalent")
  Member "a faster pseudopolynomial time algorithm for subset sum":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scheduling lower bounds via and subset sum")
  Member "space efficient string indexing for wildcard pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "orthogonal range reporting and rectangle stabbing for fat rectangles")
  Member "parallel algorithms for geometric graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "communication steps for parallel query processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "sorting searching and simulation in the mapreduce framework":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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.DC(Distributed, Parallel, and Cluster Computing)