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: "hierarchies in directed networks"
  Abstract: Interactions in many real-world phenomena can be explained by a stronghierarchical structure. Typically, this structure or ranking is not known, instead we only have observed outcomes of the interactions, and the goal is toinfer the hierarchy from these observations. Discovering a hierarchy in the context of directed networks can be formulated asfollows: given a graph, partition vertices into levels such that, ideally, there are only edges from upper levels to lower levels. The ideal case can onlyhappen if the graph is acyclic. Consequently, in practice we have to introducea penalty function that penalizes edges violating the hierarchy. A practicalvariant for such penalty is agony, where each violating edge is penalized basedon the severity of the violation. Hierarchy minimizing agony can be discoveredin O(m^2) time, and much faster in practice. In this paper we introduce severalextensions to agony. We extend the definition for weighted graphs and allow acardinality constraint that limits the number of levels. While, these areconceptually trivial extensions, current algorithms cannot handle them, northey can be easily extended. We provide an exact algorithm of O(m^2 log n) time by showing the connection of agony to the capacitated circulation problem. Wealso show that this bound is in fact pessimistic and we can compute agony forlarge datasets. In addition, we show that we can compute agony in polynomialtime for any convex penalty, and, to complete the picture, we show that minimizinghierarchy with any concave penalty is an NP-hard problem.

Connected through hyperedge HE-1 (induced by source paper "an end to end framework for cold question routing in community question answering services"):
  with members:
    - "faster way to agony discovering hierarchies in directed graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "qdee question difficulty and expertise estimation in community question answering sites"
    - "coldroute effective routing of cold questions in stack exchange sites"
    - "atp directed graph embedding with asymmetric transitivity preservation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "coldroute effective routing of cold questions in stack exchange sites"):
  with members:
    - "faster way to agony discovering hierarchies in directed graphs"
    - "qdee question difficulty and expertise estimation in community question answering sites"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "atp directed graph embedding with asymmetric transitivity preservation"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "coldroute effective routing of cold questions in stack exchange sites"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "dynamic hierarchies in temporal directed networks"):
  with members:
    - "faster way to agony discovering hierarchies in directed graphs"

Connected through hyperedge HE-5 (induced by source paper "resolution of ranking hierarchies in directed networks"):
  with members:
    - "a data driven study of influences in twitter communities"
    - "extracting tag hierarchies"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-6 (induced by source paper "detectability of ranking hierarchies in directed networks"):
  with members:
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "graph spectra and the detectability of community structure in networks"

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.DS(Data Structures and Algorithms)