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: "community detection in networks with node attributes"
  Abstract: Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.

Connected through hyperedge HE-1 (induced by source paper "finding temporal patterns using algebraic fingerprints"):
  with members:
    - "approximately counting and sampling small witnesses using a colourful decision oracle"
    - "the computational complexity of finding temporal paths under waiting time constraints"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "motifs in temporal networks"
    - "a motif based approach for identifying controversy"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "struct mmsb mixed membership stochastic blockmodels with interpretable structured priors"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "snap a general purpose network analysis and graph mining library"
    - "discriminative nonparametric latent feature relational models with data augmentation"
    - "spectral networks and locally connected networks on graphs"
    - "the nonparametric metadata dependent relational model"
    - "latent multi group membership graph model"

Connected through hyperedge HE-3 (induced by source paper "relational thematic clustering with mutually preferred neighbors"):
  with members:
    - "gromov wasserstein factorization models for graph clustering"
    - "discovering social circles in ego networks"

Connected through hyperedge HE-4 (induced by source paper "efficient algorithms for generating provably near optimal cluster descriptors for explainability"):
  with members:
    - "multi objective maximization of monotone submodular functions with cardinality constraint"

Connected through hyperedge HE-5 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-6 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "representation learning on graphs with jumping knowledge networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "relationship profiling over social networks reverse smoothness from similarity to closeness"
    - "metapad meta pattern discovery from massive text corpora"

Connected through hyperedge HE-8 (induced by source paper "topological based classification using graph convolutional networks"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "fast interactive object annotation with curve gcn"
    - "regional based query in graph active learning"
    - "pitfalls of graph neural network evaluation"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "semi supervised learning with competitive infection models"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural 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.SI(Social and Information Networks)