human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a fast method to calculate hitting time distribution for a random walk on connected undirected graph"):
  Abstract: With the advent of increasingly large graphs, we need to find a quick and reliable method to measure the distance and similarity between any pair of nodes on a large graph. One way to measure the distance is by performing random walks on a graph, and people already designed plenty of algorithms to accomplish this goal. However, most of the implementations of random walk algorithms are computationally expensive due to the use of Monte Carlo simulations which could be pretty time-consuming. Here, we propose an alternative measure of the distance between any pair of nodes on a connected and undirected graph using the notion of hitting time for a random walk. We also give an analytical solution to the hitting time distribution of a random walk on a graph. This analytical method, which can be conveniently implemented using SciPy linear algebra packages, is more time-saving to run and yields more accurate results than that obtained via Monte Carlo simulations. It is further noted that the hitting times also provide a glimpse of the community structure of a graph. This algorithm for measuring the distance between any pair of nodes is specifically devised to measure the influence of a fraudulent user upon all the other users that co-occur in the same social network. We employ our algorithm to weed out potential fraudsters from among tens of thousands of online retail users.

Includes members:
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph matching consensus")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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.DM(Discrete Mathematics)