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: "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.

Connected through hyperedge HE-1 (induced by source paper "true nonlinear dynamics from incomplete networks"):
  with members:
    - "graph size estimation"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-2 (induced by source paper "cross domain network representations"):
  with members:
    - "a framework of transferring structures across large scale information networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "deep feature learning with relative distance comparison for person re identification"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "representation learning a review and new perspectives"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "correspondent banking networks theory and experiment"):
  with members:
    - "on strongly connected digraphs with bounded cycle length"
    - "approximating the minimum equivalent digraph"

Connected through hyperedge HE-4 (induced by source paper "adult content in social live streaming services characterizing deviant users and relationships"):
  with members:
    - "on the behaviour of deviant communities in online social networks"
    - "deep residual learning for image recognition"
    - "lurking in social networks topology based analysis and ranking methods"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "like comment repin user interaction on pinterest"):
  with members:
    - "the pin bang theory discovering the pinterest world"
    - "feedback loops of attention in peer production"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-6 (induced by source paper "a survey and taxonomy of graph sampling"):
  with members:
    - "identifying communities and key vertices by reconstructing networks from samples"
    - "graph size estimation"
    - "coarse grained topology estimation via graph sampling"
    - "triangles to capture social cohesion"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "twice ramanujan sparsifiers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "practical characterization of large networks using neighborhood information"):
  with members:
    - "network sampling from static to streaming graphs"
    - "multiple random walks to uncover short paths in power law 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"

Connected through hyperedge HE-8 (induced by source paper "a measurement framework for directed networks"):
  with members:
    - "diffusion aware sampling and estimation in information diffusion networks"
    - "sampling from diffusion networks"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"

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.SI(Social and Information Networks)