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 note on the pagerank of undirected graphs"
  Abstract: The PageRank is a widely used scoring function of networks in general and of the World Wide Web graph in particular. The PageRank is defined for directed graphs, but in some special cases applications for undirected graphs occur. In the literature it is widely - but not exclusively - noted that the PageRank for undirected graphs is proportional to the degrees of the vertices of the graph. We prove that statement for a particular personalization vector in the definition of the PageRank, and we also show that in general, the PageRank of an undirected graph is not exactly proportional to the degree distribution of the graph: our main theorem gives an upper and a lower bound to the L 1 norm of the difference of the PageRank and the degree distribution vectors. A necessary and sufficient condition is also given for the PageRank for being proportional to the degree. PageRank is usually computed for directed graphs;PageRank of undirected graphs is investigated;Erroneously, some sources remark that the PR of undirected graphs are proportional to the degrees;We show that this happens very rarely;A necessary and sufficient condition is given for the PR for being proportional to the degree.

Connected through hyperedge HE-1 (induced by source paper "graph similarity using pagerank and persistent homology"):
  with members:
    - "visual detection of structural changes in time varying graphs using persistent homology"

Connected through hyperedge HE-2 (induced by source paper "line artist a multiple style sketch to painting synthesis scheme"):
  with members:
    - "style transfer via texture synthesis"
    - "photographic image synthesis with cascaded refinement networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "holistically nested edge detection"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-3 (induced by source paper "mapper on graphs for network visualization"):
  with members:
    - "persistent homology guided force directed graph layouts"
    - "statistical analysis and parameter selection for mapper"
    - "topological analysis of nerves reeb spaces mappers and multiscale mappers"
    - "visual detection of structural changes in time varying graphs using persistent homology"
    - "convergence between categorical representations of reeb space and mapper"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "local partition in rich graphs"):
  with members:
    - "community detection in networks with node attributes"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "efficient algorithms for personalized pagerank"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast bidirectional probability estimation in markov models"
    - "fast distributed pagerank computation"
    - "learning fast mixing models for structured prediction"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-6 (induced by source paper "bidirectional pagerank estimation from average case to worst case"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast bidirectional probability estimation in markov models"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "personalized pagerank with node dependent restart"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "approximating pagerank locally with sublinear query complexity"

Connected through hyperedge HE-7 (induced by source paper "fast distributed pagerank computation"):
  with members:
    - "distributed random walks"
    - "fast incremental and personalized pagerank"
    - "efficient distributed random walks with applications"

Connected through hyperedge HE-8 (induced by source paper "the multiple instances of node centrality and their implications on the vulnerability of isp networks"):
  with members:
    - "applying centrality measures to impact analysis a coauthorship network analysis"
    - "co authorship networks in the digital library research community"
    - "search in power law networks"

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)