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 "on the complexity of sampling nodes uniformly from a graph"):
  Abstract: We study a number of graph exploration problems in the following natural scenario: an algorithm starts exploring an undirected graph from some seed node; the algorithm, for an arbitrary node $v$ that it is aware of, can ask an oracle to return the set of the neighbors of $v$. (In social network analysis, a call to this oracle corresponds to downloading the profile page of user $v$ in a social network.) The goal of the algorithm is to either learn something (e.g., average degree) about the graph, or to return some random function of the graph (e.g., a uniform-at-random node), while accessing/downloading as few nodes of the graph as possible. Motivated by practical applications, we study the complexities of a variety of problems in terms of the graph's mixing time and average degree -- two measures that are believed to be quite small in real-world social networks, and that have often been used in the applied literature to bound the performance of online exploration algorithms. Our main result is that the algorithm has to access $\Omega\left(t_{\rm mix} d_{\rm avg} \epsilon^{-2} \ln \delta^{-1}\right)$ nodes to obtain, with probability at least $1-\delta$, an $\epsilon$-additive approximation of the average of a bounded function on the nodes of a graph -- this lower bound matches the performance of an algorithm that was proposed in the literature. We also give tight bounds for the problem of returning a close-to-uniform-at-random node from the graph. Finally, we give lower bounds for the problems of estimating the average degree of the graph, and the number of nodes of the graph.

Includes members:
  Member "how large is your graph":
    also connected through other hyperedges:
      (none)
  Member "on sampling edges almost uniformly":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computing and testing small connectivity in near linear time and queries via fast local cut algorithms")
  Member "simple set cardinality estimation through random sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved cardinality estimation by learning queries containment rates")
  Member "wedge sampling for computing clustering coefficients and triangle counts on large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the power of pivoting for exact clique counting")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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.DS(Data Structures and Algorithms)