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: "on the bias of bfs"
  Abstract: Breadth First Search (BFS) and other graph traversal techniques are widely used for measuring large unknown graphs, such as online social networks. It has been empirically observed that an incomplete BFS is biased toward high degree nodes. In contrast to more studied sampling techniques, such as random walks, the precise bias of BFS has not been characterized to date. In this paper, we 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 of covered nodes, in a random graph $RG(p_k)$ with a given degree distribution $p_k$. Furthermore, we also show that, for $RG(p_k)$, all commonly used graph traversal techniques (BFS, DFS, Forest Fire, and Snowball Sampling) lead to the same bias, and we show how to correct for this bias. To give a broader perspective, we compare this class of exploration techniques to random walks that are well-studied and easier to analyze. Next, we study by simulation the effect of graph properties not captured directly by our model. We find that the bias gets amplified in graphs with strong positive assortativity. Finally, we demonstrate the above results by sampling the Facebook social network, and we provide some practical guidelines for graph sampling in practice.

Connected through hyperedge HE-1 (induced by source paper "sampling promotes community structure in social and information networks"):
  with members:
    - "assessing the effectiveness of real world network simplification"
    - "network based statistical comparison of citation topology of bibliographic databases"
    - "node mixing and group structure of complex software networks"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-2 (induced by source paper "graph size estimation"):
  with members:
    - "social structure of facebook networks"
    - "coarse grained topology estimation via graph sampling"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "what is the real size of a sampled network the case of the internet"

Connected through hyperedge HE-3 (induced by source paper "diffusion aware sampling and estimation in information diffusion networks"):
  with members:
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "towards unbiased bfs sampling"):
  with members:
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a radar for the internet"
    - "leveraging social network infrastructure to improve peer to peer overlay performance results from orkut"

Connected through hyperedge HE-5 (induced by source paper "walking on a graph with a magnifying glass stratified sampling via weighted random walks"):
  with members:
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-6 (induced by source paper "benefits of bias towards better characterization of network sampling"):
  with members:
    - "expansion and search in networks"
    - "a walk in facebook uniform sampling of users in online social networks"
    - "search in power law networks"

Connected through hyperedge HE-7 (induced by source paper "multigraph sampling of online social networks"):
  with members:
    - "towards unbiased bfs sampling"
    - "folks in folksonomies social link prediction from shared metadata"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-8 (induced by source paper "a walk in facebook uniform sampling of users in online social networks"):
  with members:
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "a comparison of techniques for sampling web pages"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)