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: "edmot an edge enhancement approach for motif aware community detection"
  Abstract: Network community detection is a hot research topic in network analysis. Although many methods have been proposed for community detection, most of them only take into consideration the lower-order structure of the network at the level of individual nodes and edges. Thus, they fail to capture the higher-order characteristics at the level of small dense subgraph patterns, e.g., motifs. Recently, some higher-order methods have been developed but they typically focus on the motif-based hypergraph which is assumed to be a connected graph. However, such assumption cannot be ensured in some real-world networks. In particular, the hypergraph may become fragmented. That is, it may consist of a large number of connected components and isolated nodes, despite the fact that the original network is a connected graph. Therefore, the existing higher-order methods would suffer seriously from the above fragmentation issue, since in these approaches, nodes without connection in hypergraph can't be grouped together even if they belong to the same community. To address the above fragmentation issue, we propose an Edge enhancement approach for Motif-aware community detection (EdMot ). The main idea is as follows. Firstly, a motif-based hypergraph is constructed and the top K largest connected components in the hypergraph are partitioned into modules. Afterwards, the connectivity structure within each module is strengthened by constructing an edge set to derive a clique from each module. Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed. Finally, the rewired network is partitioned to obtain the higher-order community structure. Extensive experiments have been conducted on eight real-world datasets and the results show the effectiveness of the proposed method in improving the community detection performance of state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  with members:
    - "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"
    - "heterogeneous network motifs"
    - "hypergraph clustering with categorical edge labels"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "rept a streaming algorithm of approximating global and local triangle counts in parallel"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"

Connected through hyperedge HE-2 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "multi scale attributed node embedding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "pytorch an imperative style high performance deep learning library"
    - "netlsd hearing the shape of a graph"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a simple baseline algorithm for graph classification"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-3 (induced by source paper "adversarial enhancement for community detection in complex networks"):
  with members:
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "node2vec scalable feature learning for networks"
    - "enhancing community detection using a network weighting strategy"
    - "efficient and principled method for detecting communities in 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)