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: "a survey and taxonomy of graph sampling"
  Abstract: Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. It has a wide spectrum of applications, e.g. survey hidden population in sociology [54], visualize social graph [29], scale down Internet AS graph [27], graph sparsification [8], etc. In some scenarios, the whole graph is known and the purpose of sampling is to obtain a smaller graph. In other scenarios, the graph is unknown and sampling is regarded as a way to explore the graph. Commonly used techniques are Vertex Sampling, Edge Sampling and Traversal Based Sampling. We provide a taxonomy of different graph sampling objectives and graph sampling approaches. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided. Although being smaller in size, sampled graphs may be similar to original graphs in some way. We are particularly interested in what graph properties are preserved given a sampling procedure. If some properties are preserved, we can estimate them on the sampled graphs, which gives a way to construct efficient estimators. If one algorithm relies on the perserved properties, we can expect that it gives similar output on original and sampled graphs. This leads to a systematic way to accelerate a class of graph algorithms. In this survey, we discuss both classical text-book type properties and some advanced properties. The landscape is tabularized and we see a lot of missing works in this field. Some theoretical studies are collected in this survey and simple extensions are made. Most previous numerical evaluation works come in an ad hoc fashion, i.e. evaluate different type of graphs, different set of properties, and different sampling algorithms. A systematical and neutral evaluation is needed to shed light on further graph sampling studies.

Connected through hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-2 (induced by source paper "graphsaint graph sampling based inductive learning method"):
  with members:
    - "graphact accelerating gcn training on cpu fpga heterogeneous platforms"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "accurate efficient and scalable graph embedding"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-3 (induced by source paper "star sampling with and without replacement"):
  with members:
    - "a markov chain model for the search time for max degree nodes in a graph using a biased random walk"
    - "quick detection of nodes with large degrees"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "search in power law networks"

Connected through hyperedge HE-4 (induced by source paper "modeling spread of preferences in social networks for sampling based preference aggregation"):
  with members:
    - "preference elicitation for general random utility models"
    - "discrete signal processing on graphs"
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-5 (induced by source paper "weighted edge sampling for static graphs"):
  with members:
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-6 (induced by source paper "veilgraph streaming graph approximations"):
  with members:
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "origraph interactive network wrangling"):
  with members:
    - "taggle combining overview and details in tabular data visualizations"
    - "jacob s ladder the user implications of leveraging graph pivots"
    - "juniper a tree table approach to multivariate graph visualization"
    - "taggle scalable visualization of tabular data through aggregation"
    - "graffinity visualizing connectivity in large graphs"

Connected through hyperedge HE-8 (induced by source paper "high throughput cryptocurrency routing in payment channel networks"):
  with members:
    - "flash efficient dynamic routing for offchain networks"
    - "settling payments fast and private efficient decentralized routing for path based transactions"

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)