human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "ms2 a molecular simulation tool for thermodynamic properties new version release"
  Abstract: Abstract   A new version release (2.0) of the molecular simulation tool ms2 [S. Deublein et al., Comput. Phys. Commun. 182 (2011) 2350] is presented. Version 2.0 of ms2 features a hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation to achieve higher scalability. Furthermore, the formalism by Lustig [R. Lustig, Mol. Phys. 110 (2012) 3041] is implemented, allowing for a systematic sampling of Massieu potential derivatives in a single simulation run. Moreover, the Green–Kubo formalism is extended for the sampling of the electric conductivity and the residence time. To remove the restriction of the preceding version to electro-neutral molecules, Ewald summation is implemented to consider ionic long range interactions. Finally, the sampling of the radial distribution function is added.    Program summary  Program title:    m  s  2     Catalogue identifier: AEJF_v2_0  Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJF_v2_0.html   Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland  Licensing provisions: Standard CPC licence,  http://cpc.cs.qub.ac.uk/licence/licence.html   No. of lines in distributed program, including test data, etc.: 50375  No. of bytes in distributed program, including test data, etc.: 345786  Distribution format: tar.gz  Programming language: Fortran90.  Computer: The simulation program    m  s  2    is usable on a wide variety of platforms, from single processor machines to modern supercomputers.  Operating system: Unix/Linux.  Has the code been vectorized or parallelized?: Yes: Message Passing Interface (MPI) protocol and OpenMP Scalability is up to 2000 cores.  RAM:    m  s  2    runs on single cores with 512 MB RAM. The memory demand rises with increasing number of cores used per node and increasing number of molecules.  Classification: 7.7, 7.9, 12.  External routines: Message Passing Interface (MPI)  Catalogue identifier of previous version: AEJF_v1_0  Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 2350  Does the new version supersede the previous version?: Yes.  Nature of problem: Calculation of application oriented thermodynamic properties for fluids consisting of rigid molecules: vapor–liquid equilibria of pure fluids and multi-component mixtures, thermal and caloric data as well as transport properties.  Solution method: Molecular dynamics, Monte Carlo, various classical ensembles, grand equilibrium method, Green–Kubo formalism, Lustig formalism  Reasons for new version: The source code was extended to introduce new features.  Summary of revisions: The new features of Version 2.0 include: Hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation; Ewald summation for long range interactions; sampling of Massieu potential derivatives; extended Green–Kubo formalism for the sampling of the electric conductivity and the residence time; radial distribution function.  Restrictions: None. The system size is user-defined. Typical problems addressed by    m  s  2    can be solved by simulating systems containing typically 1000–4000 molecules.  Unusual features: Auxiliary feature tools are available for creating input files, analyzing simulation results and visualizing molecular trajectories.  Additional comments: Sample makefiles for multiple operation platforms are provided. Documentation is provided with the installation package and is available at  http://www.ms-2.de .  Running time: The running time of    m  s  2    depends on the specified problem, the system size and the number of processes used in the simulation. E.g. running four processes on a “Nehalem” processor, simulations calculating vapor–liquid equilibrium data take between two and 12 hours, calculating transport properties between six and 24 hours. Note that the examples given above stand for the total running time as there is no post-processing of any kind involved in property calculations.

Connected through hyperedge HE-1 (induced by source paper "a workflow for fast evaluation of mapping heuristics targeting cloud infrastructures"):
  with members:
    - "modeling and simulation of scalable cloud computing environments and the cloudsim toolkit challenges and opportunities"

Question: Please tell me which class the center node 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.CE(Computational Engineering, Finance, and Science)