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 "towards unbiased bfs sampling"):
  Abstract: Breadth First Search (BFS) is a widely used approach for sampling large unknown Internet topologies. Its main advantage over random walks and other exploration techniques is that a BFS sample is a plausible graph on its own, and therefore we can study its topological characteristics. However, it has been empirically observed that incomplete BFS is biased toward high-degree nodes, which may strongly affect the measurements. In this paper, we first analytically quantify the degree bias of BFS sampling. In particular, we calculate the node degree distribution expected to be observed by BFS as a function of the fraction f of covered nodes, in a random graph RG(pk) with an arbitrary degree distribution pk. We also show that, for RG(pk), all commonly used graph traversal techniques (BFS, DFS, Forest Fire, Snowball Sampling, RDS) suffer from exactly the same bias. Next, based on our theoretical analysis, we propose a practical BFS-bias correction procedure. It takes as input a collected BFS sample together with its fraction f. Even though RG(pk) does not capture many graph properties common in real-life graphs (such as assortativity), our RG(pk)-based correction technique performs well on a broad range of Internet topologies and on two large BFS samples of Facebook and Orkut networks. Finally, we consider and evaluate a family of alternative correction procedures, and demonstrate that, although they are unbiased for an arbitrary topology, their large variance makes them far less effective than the RG(pk)-based technique.

Includes members:
  Member "walking on a graph with a magnifying glass stratified sampling via weighted random walks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "star sampling with and without replacement")
  Member "multigraph sampling of online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling online social networks by random walk with indirect jumps")
  Member "estimating and sampling graphs with multidimensional random walks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")
  Member "on the bias of bfs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sampling promotes community structure in social and information networks")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "a radar for the internet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast dynamics in internet topology preliminary observations and explanations")
  Member "leveraging social network infrastructure to improve peer to peer overlay performance results from orkut":
    also connected through other hyperedges:
      (none)

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.SI(Social and Information Networks)