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: "degree relations of triangles in real world networks and graph models"
  Abstract: Triangles are an important building block and distinguishing feature of real-world networks, but their structure is still poorly understood. Despite numerous reports on the abundance of triangles, there is very little information on what these triangles look like. We initiate the study of degree-labeled triangles, - specifically, degree homogeneity versus heterogeneity in triangles. This yields new insight into the structure of real-world graphs. We observe that networks coming from social and collaborative situations are dominated by homogeneous triangles, i.e., degrees of vertices in a triangle are quite similar to each other. On the other hand, information networks (e.g., web graphs) are dominated by heterogeneous triangles, i.e., the degrees in triangles are quite disparate. Surprisingly, nodes within the top 1% of degrees participate in the vast majority of triangles in heterogeneous graphs. We investigate whether current graph models reproduce the types of triangles that are observed in real data and observe that most models fail to accurately capture these salient features.

Connected through hyperedge HE-1 (induced by source paper "retrieving top weighted triangles in graphs"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "simplicial closure and higher order link prediction"
    - "tiered sampling an efficient method for approximate counting sparse motifs in massive graph streams"
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-2 (induced by source paper "directed random geometric graphs"):
  with members:
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-3 (induced by source paper "escape efficiently counting all 5 vertex subgraphs"):
  with members:
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "fast parallel graphlet counting for large networks"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-4 (induced by source paper "large graph models a review"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "a scalable generative graph model with community structure"
    - "using triangles to improve community detection in directed networks"
    - "an in depth analysis of stochastic kronecker graphs"
    - "external memory based distributed generation of massive scale social networks on small clusters"

Connected through hyperedge HE-5 (induced by source paper "tensor spectral clustering for partitioning higher order network structures"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "using triangles to improve community detection in directed networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "clustering and community detection in directed networks a survey"
    - "tensor decompositions for learning latent variable models"
    - "shaping communities out of triangles"
    - "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links"
    - "most tensor problems are np hard"

Connected through hyperedge HE-6 (induced by source paper "evolution of directed triangle motifs in the google osn"):
  with members:
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "google or google dissecting the evolution of the new osn in its first year"
    - "multigraph sampling of online social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-7 (induced by source paper "self coordinated corona graphs a model for complex networks"):
  with members:
    - "ergm graphlets a package for erg modeling based on graphlet statistics"

Connected through hyperedge HE-8 (induced by source paper "context dependent preferential attachment model for complex networks"):
  with members:
    - "technological networks and the spread of computer viruses"

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)