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: "scalable text and link analysis with mixed topic link models"
  Abstract: Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.

Connected through hyperedge HE-1 (induced by source paper "stochastic blockmodels with edge information"):
  with members:
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "from community detection to community profiling"):
  with members:
    - "from group recommendations to group formation"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "efficient community detection in large networks using content and links"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-3 (induced by source paper "latent space model for multi modal social data"):
  with members:
    - "structure and inference in annotated networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "traveling trends social butterflies or frequent fliers"

Connected through hyperedge HE-4 (induced by source paper "bibliographic analysis on research publications using authors categorical labels and the citation network"):
  with members:
    - "twitter network topic model a full bayesian treatment for social network and text modeling"
    - "bibliographic analysis with the citation network topic model"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-5 (induced by source paper "bibliographic analysis with the citation network topic model"):
  with members:
    - "twitter network topic model a full bayesian treatment for social network and text modeling"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-6 (induced by source paper "evaluating the effect of topic consideration in identifying communities of rating based social networks"):
  with members:
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-7 (induced by source paper "partitioning networks with node attributes by compressing information flow"):
  with members:
    - "spectral clustering with epidemic diffusion"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "image labeling on a network using social network metadata for image classification"
    - "efficient community detection in large networks using content and links"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "community specific temporal topic discovery from social media"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "understanding the interaction between interests conversations and friendships in facebook"
    - "efficient community detection in large networks using content and links"
    - "empirical comparison of algorithms for network community detection"

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.LG(Machine Learning)