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: "simple set cardinality estimation through random sampling"
  Abstract: We present a simple algorithm that estimates the cardinality $n$ of a set $V$ when allowed to sample elements of $V$ uniformly and independently at random. Our algorithm with probability $(1-\delta)$ returns a $(1\pm\epsilon)-$approximation of $n$ drawing $O\big(\sqrt{n} \cdot \epsilon^{-1}\sqrt{\log(\delta^{-1})}\big)$ samples (for $\epsilon^{-1}\sqrt{\log(\delta^{-1})} = O(\sqrt{n})$).

Connected through hyperedge HE-1 (induced by source paper "improved cardinality estimation by learning queries containment rates"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "the case for learned index structures"
    - "overcoming catastrophic forgetting in neural networks"
    - "a fast unified model for parsing and sentence understanding"

Connected through hyperedge HE-2 (induced by source paper "community exploration from offline optimization to online learning"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of sampling nodes uniformly from a graph"):
  with members:
    - "how large is your graph"
    - "on sampling edges almost uniformly"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "sublinear algorithms for local graph centrality estimation"):
  with members:
    - "on approximating the stationary distribution of time reversible markov chains"
    - "an optimization approach to locally biased graph algorithms"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "the power of local information in pagerank"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based community detection"

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.DM(Discrete Mathematics)