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: "estimating and sampling graphs with multidimensional random walks"
  Abstract: Estimating characteristics of large graphs via sampling is a vital part of the study of complex networks. Current sampling methods such as (independent) random vertex and random walks are useful but have drawbacks. Random vertex sampling may require too many resources (time, bandwidth, or money). Random walks, which normally require fewer resources per sample, can suffer from large estimation errors in the presence of disconnected or loosely connected graphs. In this work we propose a new $m$-dimensional random walk that uses $m$ dependent random walkers. We show that the proposed sampling method, which we call Frontier sampling, exhibits all of the nice sampling properties of a regular random walk. At the same time, our simulations over large real world graphs show that, in the presence of disconnected or loosely connected components, Frontier sampling exhibits lower estimation errors than regular random walks. We also show that Frontier sampling is more suitable than random vertex sampling to sample the tail of the degree distribution of the graph.

Connected through hyperedge HE-1 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms"):
  with members:
    - "automatic compiler based fpga accelerator for cnn training"
    - "graphsaint graph sampling based inductive learning method"
    - "link prediction based on graph neural networks"
    - "accurate efficient and scalable graph embedding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-2 (induced by source paper "learning graph embedding with limited labeled data an efficient sampling approach"):
  with members:
    - "graph transformer networks"
    - "simplifying graph convolutional networks"
    - "pre training graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "topology adaptive graph convolutional networks"
    - "sampling online social networks by random walk with indirect jumps"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "true nonlinear dynamics from incomplete networks"):
  with members:
    - "graph size estimation"
    - "towards unbiased bfs sampling"

Connected through hyperedge HE-4 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"

Connected through hyperedge HE-5 (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-6 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "conjoining uncooperative societies facilitates evolution of cooperation"
    - "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "engagement dynamics and sensitivity analysis of youtube videos"
    - "the majority illusion in social networks"

Connected through hyperedge HE-7 (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"
    - "a survey and taxonomy of graph sampling"
    - "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"
    - "search in power law networks"

Connected through hyperedge HE-8 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox"):
  with members:
    - "information diffusion in social networks friendship paradox based models and statistical inference"
    - "friendship paradox biases perceptions in directed networks"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "gemsec graph embedding with self clustering"
    - "the majority illusion in social networks"
    - "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"

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.DS(Data Structures and Algorithms)