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: "communities of minima in local optima networks of combinatorial spaces"
  Abstract: In this work we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we shortly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.

Connected through hyperedge HE-1 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes"):
  with members:
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "local optima networks and the performance of iterated local search"
    - "local optima networks landscape autocorrelation and heuristic search performance"
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "the connectivity of nk landscapes basins a network analysis"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-2 (induced by source paper "local optima networks and the performance of iterated local search"):
  with members:
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-3 (induced by source paper "local optima networks landscape autocorrelation and heuristic search performance"):
  with members:
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "elementary landscape decomposition of the quadratic assignment problem"

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.NE(Neural and Evolutionary Computing)