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: "structure and overlaps of communities in networks"
  Abstract: One of the main organizing principles in real-world social, information and technological networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. Due to unavailability of labeled ground-truth data it is practically impossible to evaluate and compare different models and notions of communities on a large scale. #R##N#In this paper we identify 6 large social, collaboration, and information networks where nodes explicitly state their community memberships. We define ground-truth communities by using these explicit memberships. We then empirically study how such ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities, in contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. #R##N#Existing models of network communities do not capture dense community overlaps. We present the Community-Affiliation Graph Model (AGM), a conceptual model of network community structure, which reliably captures the overall structure of networks as well as the overlapping nature of network communities.

Connected through hyperedge HE-1 (induced by source paper "core periphery structure in directed networks"):
  with members:
    - "a nonlinear spectral method for core periphery detection in networks"
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"

Connected through hyperedge HE-2 (induced by source paper "community detection in partially observable social networks"):
  with members:
    - "provable and practical approximations for the degree distribution using sublinear graph samples"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "estimation of simultaneously sparse and low rank matrices"
    - "defining and evaluating network communities based on ground truth"
    - "finding missing edges and communities in incomplete networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-3 (induced by source paper "an enhanced multi objective biogeography based optimization algorithm for automatic detection of overlapping communities in a social network with node attributes"):
  with members:
    - "evaluating the effect of topic consideration in identifying communities of rating based social networks"
    - "partitioning networks with node attributes by compressing information flow"
    - "towards linear time overlapping community detection in social networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"
    - "empirical comparison of algorithms for network community detection"
    - "detect overlapping and hierarchical community structure in networks"

Connected through hyperedge HE-4 (induced by source paper "detection of core periphery structure in networks using spectral methods and geodesic paths"):
  with members:
    - "centrality metrics and localization in core periphery networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "role discovery in networks"
    - "a local perspective on community structure in multilayer networks"
    - "core periphery structure in networks"
    - "social structure of facebook networks"
    - "eigenvector synchronization graph rigidity and the molecule problem"

Connected through hyperedge HE-5 (induced by source paper "community detection in networks with node attributes"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "demon a local first discovery method for overlapping communities"
    - "latent multi group membership graph model"
    - "efficient community detection in large networks using content and links"
    - "neighborhoods are good communities"
    - "infinite multiple membership relational modeling for complex networks"

Connected through hyperedge HE-6 (induced by source paper "benchmarking community detection methods on social media data"):
  with members:
    - "social structure of facebook networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "defining and evaluating network communities based on ground truth"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "neighborhoods are good communities"
    - "empirical comparison of algorithms for network community detection"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-8 (induced by source paper "core periphery structure in networks"):
  with members:
    - "efficient and principled method for detecting communities in networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)