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 "the minimization of random hypergraphs"):
  Abstract: We investigate the maximum-entropy model $\mathcal{B}_{n,m,p}$ for random $n$-vertex, $m$-edge multi-hypergraphs with expected edge size $pn$. We show that the expected number of inclusion-wise minimal edges of $\mathcal{B}_{n,m,p}$ undergoes a phase transition with respect to $m$. If $m \le 1/(1-p)^{(1-p)n}$, the expectation is of order $\operatorname{O}(m)$, while for $m \ge 1/(1-p)^{(1-p)n}$, it is $\operatorname{O}(2^{(\operatorname{H}(\alpha) + (1-\alpha) \operatorname{ld} p)n})$. Here, $\operatorname{H}$ denotes the binary entropy function and $\alpha = - (\log_{1-p} m)/n$. This implies that the maximum expected number of minimal edges over all $m$ is $\operatorname{O}((1+p)^n)$. All asymptotics are with respect to $n$, for all upper bounds we have (almost) matching lower bounds. As a technical contribution, we establish the fact that the probability of a set of cardinality $i$ being minimal after $m$ i.i.d. maximum-entropy trials exhibits a sharp threshold behavior at $i^* = n + \log_{1-p} m$, which is of independent interest. Our structural findings have algorithmic implications, for example, for computing the minimal hitting sets of a hypergraph as well as the profiling of relational databases.

Includes members:
  Member "probabilistic tools for the analysis of randomized optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "working principles of binary differential evolution")
  Member "efficiently enumerating hitting sets of hypergraphs arising in data profiling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space")

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)