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 "automated derivation of parametric data movement lower bounds for affine programs"):
  Abstract: For most relevant computation, the energy and time needed for data movement dominates that for performing arithmetic operations on all computing systems today. Hence it is of critical importance to understand the minimal total data movement achievable during the execution of an algorithm. The achieved total data movement for different schedules of an algorithm can vary widely depending on how efficiently the cache is used, e.g., untiled versus effectively tiled matrix-matrix multiplication. A significant current challenge is that no existing tool is able to meaningfully quantify the potential reduction to the data movement of a computation that can be achieved by more effective use of the cache through operation rescheduling. Asymptotic parametric expressions of data movement lower bounds have previously been manually derived for a limited number of algorithms, often without scaling constants. In this paper, we present the first compile-time approach for deriving non-asymptotic parametric expressions of data movement lower bounds for arbitrary affine computations. The approach has been implemented in a fully automatic tool (IOLB) that can generate these lower bounds for input affine programs. IOLB's use is demonstrated by exercising it on all the benchmarks of the PolyBench suite. The advantages of IOLB are many: (1) IOLB enables us to derive bounds for few dozens of algorithms for which these lower bounds have never been derived. This reflects an increase of productivity by automation. (2) Anyone is able to obtain these lower bounds through IOLB, no expertise is required. (3) For some of the most well-studied algorithms, the lower bounds obtained by \tool are higher than any previously reported manually derived lower bounds.

Includes members:
  Member "pushing the bounds for matrix matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "red blue pebbling revisited near optimal parallel matrix matrix multiplication")
  Member "on characterizing the data movement complexity of computational dags for parallel execution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on characterizing the data access complexity of programs")
  Member "minimizing communication in linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons")
  Member "revisiting matrix product on master worker platforms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the momms family of matrix multiplication algorithms")
  Member "introduction to the ginac framework for symbolic computation within the c programming language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the fly optimization of parallel computation of symbolic symplectic invariants")

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)