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: "producing a unified graph representation from multiple social network views"
  Abstract: In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produce a single unified graph representation, based on the combination of the k-nearest neighbour sets for users derived from each view. These views can be either relation-based or feature-based. The proposed method is evaluated on a number of annotated multi-view Twitter datasets, where it is shown to support the discovery of the underlying community structure in the data.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hierarchical stochastic block model for community detection in multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a degree centrality in multi layered social network"

Connected through hyperedge HE-3 (induced by source paper "mvn2vec preservation and collaboration in multi view network embedding"):
  with members:
    - "representation learning for attributed multiplex heterogeneous network"
    - "a structural representation learning for multi relational networks"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-4 (induced by source paper "stochastic block models with multiple continuous attributes"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "link prediction in social networks the state of the art"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"
    - "comparative study for inference of hidden classes in stochastic block models"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-5 (induced by source paper "clustering network layers with the strata multilayer stochastic block model"):
  with members:
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "core periphery structure in networks"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "analysis of multiview legislative networks with structured matrix factorization does twitter influence translate to the real world"):
  with members:
    - "structural and functional discovery in dynamic networks with non negative matrix factorization"
    - "community detection in networks with node attributes"
    - "identifying topical twitter communities via user list aggregation"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-7 (induced by source paper "online social media in the syria conflict encompassing the extremes and the in betweens"):
  with members:
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "aggregating content and network information to curate twitter user lists"

Connected through hyperedge HE-8 (induced by source paper "the extreme right filter bubble"):
  with members:
    - "uncovering the wider structure of extreme right communities spanning popular online networks"
    - "an analysis of interactions within and between extreme right communities in social media"

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)