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: "community detection in multiplex networks using locally adaptive random walks"
  Abstract: Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep node ranking structural network embedding and end to end node classification"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "attention is all you need"
    - "predicting multicellular function through multi layer tissue networks"
    - "a unified approach to interpreting model predictions"
    - "a survey on network embedding"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"

Connected through hyperedge HE-3 (induced by source paper "community detection in multiplex networks"):
  with members:
    - "overlapping community detection by local decentralised vertex centred process"
    - "node centric community detection in multilayer networks with layer coverage diversification bias"
    - "mapping higher order network flows in memory and multilayer networks with infomap"
    - "clustering attributed graphs models measures and methods"
    - "a local perspective on community structure in multilayer networks"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "modularity in multilayer networks using redundancy based resolution and projection based inter layer coupling"):
  with members:
    - "consensus community detection in multilayer networks using parameter free graph pruning"
    - "revisiting resolution and inter layer coupling factors in modularity for multilayer networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-5 (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"
    - "producing a unified graph representation from multiple social network views"
    - "a degree centrality in multi layered social network"

Connected through hyperedge HE-6 (induced by source paper "revisiting resolution and inter layer coupling factors in modularity for multilayer networks"):
  with members:
    - "modularity in complex multilayer networks with multiple aspects a static perspective"

Connected through hyperedge HE-7 (induced by source paper "a local perspective on community structure in multilayer networks"):
  with members:
    - "block models and personalized pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "spreading processes in multilayer networks"
    - "seeded pagerank solution paths"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "pagerank beyond the web"
    - "ranking and clustering of nodes in networks with smart teleportation"
    - "defining and evaluating network communities based on ground truth"

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)