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: "mayavi 3d visualization of scientific data"
  Abstract: Mayavi is a general purpose, open source 3D scientific visualization package that is tightly integrated with the rich ecosystem of Python scientific packages. Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications.

Connected through hyperedge HE-1 (induced by source paper "multiscale finite element calculations in python using sfepy"):
  with members:
    - "firedrake automating the finite element method by composing abstractions"
    - "multiscale computing in the exascale era"
    - "enhancing sfepy with isogeometric analysis"
    - "petiga a framework for high performance isogeometric analysis"

Connected through hyperedge HE-2 (induced by source paper "scipy 1 0 fundamental algorithms for scientific computing in python"):
  with members:
    - "scikit image image processing in python"
    - "the numpy array a structure for efficient numerical computation"
    - "analysis of approximate nearest neighbor searching with clustered point sets"

Connected through hyperedge HE-3 (induced by source paper "graying the black box understanding dqns"):
  with members:
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "increasing the action gap new operators for reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

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.SE(Software Engineering)