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: "faster way to agony discovering hierarchies in directed graphs"
  Abstract: Many real-world phenomena exhibit strong hierarchical structure. Consequently, in many real-world directed social networks vertices do not play equal role. Instead, vertices form a hierarchy such that the edges appear mainly from upper levels to lower levels. Discovering hierarchies from such graphs is a challenging problem that has gained attention. Formally, given a directed graph, we want to partition vertices into levels such that ideally there are only edges from upper levels to lower levels. From computational point of view, the ideal case is when the underlying directed graph is acyclic. In such case, we can partition the vertices into a hierarchy such that there are only edges from upper levels to lower edges. In practice, graphs are rarely acyclic, hence we need to penalize the edges that violate the hierarchy. One practical approach is agony, where each violating edge is penalized based on the severity of the violation. The fastest algorithm for computing agony requires $O(nm^2)$ time. In the paper we present an algorithm for computing agony that has better theoretical bound, namely $O(m^2)$. We also show that in practice the obtained bound is pessimistic and that we can use our algorithm to compute agony for large datasets. Moreover, our algorithm can be used as any-time algorithm.

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:
    - "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"
    - "hierarchies in directed networks"

Connected through hyperedge HE-2 (induced by source paper "coldroute effective routing of cold questions in stack exchange sites"):
  with members:
    - "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"
    - "hierarchies in directed 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 "probabilistic causal analysis of social influence"):
  with members:
    - "efficient simulation of financial stress testing scenarios with suppes bayes causal networks"
    - "exposing the probabilistic causal structure of discrimination"
    - "distinguishing between personal preferences and social influence in online activity feeds"
    - "a data based approach to social influence maximization"
    - "nectarss an rss feed ranking system that implicitly learns user preferences"

Connected through hyperedge HE-4 (induced by source paper "dynamic hierarchies in temporal directed networks"):
  with members:
    - "hierarchies in directed 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)