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: "abacus frequent pattern mining based community discovery in multidimensional networks"
  Abstract: Community Discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities can exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes can exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group highly correlated nodes, even if they might not be connected in any of the monodimensional networks. We devise ABACUS (Apriori-BAsed Community discoverer in mUltidimensional networkS), an algorithm that is able to extract multidimensional communities based on the apriori itemset miner applied to monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.

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 "finding events in temporal networks segmentation meets densest subgraph discovery"):
  with members:
    - "strongly polynomial efficient approximation scheme for segmentation"
    - "evidense a graph based method for finding unique high impact events with succinct keyword based descriptions"
    - "local community detection in dynamic networks"
    - "best friends forever bff finding lasting dense subgraphs"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-3 (induced by source paper "quantifying layer similarity in multiplex networks a systematic study"):
  with members:
    - "spreading processes in multilayer networks"

Connected through hyperedge HE-4 (induced by source paper "principled multilayer network embedding"):
  with members:
    - "spectral clustering methods for multiplex networks"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "name disambiguation in anonymized graphs using network embedding"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multilayer spectral graph clustering via convex layer aggregation"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "clustering network layers with the strata multilayer stochastic block model"

Connected through hyperedge HE-5 (induced by source paper "multimodal clustering for community detection"):
  with members:
    - "algorithms for graph constrained coalition formation in the real world"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "towards a closure operator for enumeration of maximal tricliques in tripartite hypergraphs"
    - "towards a unified taxonomy of biclustering methods"
    - "from below approximations in boolean matrix factorization geometry and new algorithm"
    - "clustering boolean tensors"
    - "introduction to formal concept analysis and its applications in information retrieval and related fields"
    - "concept stability for constructing taxonomies of web site users"

Connected through hyperedge HE-6 (induced by source paper "flexible constrained sampling with guarantees for pattern mining"):
  with members:
    - "constrained sampling and counting universal hashing meets sat solving"
    - "distribution aware sampling and weighted model counting for sat"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"

Connected through hyperedge HE-7 (induced by source paper "finding theme communities from database networks"):
  with members:
    - "alid scalable dominant cluster detection"
    - "community detection in networks with node attributes"
    - "truss decomposition in massive networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-8 (induced by source paper "finding theme communities from database networks from mining to indexing and query answering"):
  with members:
    - "alid scalable dominant cluster detection"
    - "community detection in networks with node attributes"
    - "truss decomposition in massive 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)