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 "connectivity in random annulus graphs and the geometric block model"):
  Abstract: Random geometric graphs are the simplest, and perhaps the earliest possible random graph model of spatial networks, introduced by Gilbert in 1961. In the most basic setting, a random geometric graph $G(n,r)$ has $n$ vertices. Each vertex of the graph is assigned a real number in $[0,1]$ randomly and uniformly. There is an edge between two vertices if the corresponding two random numbers differ by at most $r$ (to mitigate the boundary effect, let us consider the Lee distance here, $d_L(u,v) = \min\{|u-v|, 1-|u-v|\}$). It is well-known that the connectivity threshold regime for random geometric graphs is at $r \approx \frac{\log n}{n}$. In particular, if $r = \frac{a\log n}{n}$, then a random geometric graph is connected with high probability if and only if $a > 1$. Consider $G(n,\frac{(1+\epsilon)\log{n}}{n})$ for any $\epsilon >0$ to satisfy the connectivity requirement and delete half of its edges which have distance at most $\frac{\log{n}}{2n}$. It is natural to believe that the resultant graph will be disconnected. Surprisingly, we show that the graph still remains connected! #R##N#Formally, generalizing random geometric graphs, we define a random annulus graph $G(n, [r_1, r_2]), r_1   \frac12$ and $a >1$. That is $G(n, [0,\frac{0.99\log n}{n}])$ is not connected but $G(n,[\frac{0.50 \log n}{n},\frac{1+\epsilon \log n}{n}])$ is. #R##N#This result is then used to give improved lower and upper bounds on the recovery threshold of the geometric block model.

Includes members:
  Member "the geometric block model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active learning in the geometric block model")
  Member "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized spectral clustering in large scale stochastic block models")

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)