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: "hidden community detection in social networks"
  Abstract: We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden community structure. #R##N#We present a novel approach called HICODE (HIdden COmmunity DEtection) that identifies the hidden community structure as well as the dominant community structure. By weakening the strength of the dominant structure, one can uncover the hidden structure beneath. Likewise, by reducing the strength of the hidden structure, one can more accurately identify the dominant structure. In this way, HICODE tackles both tasks simultaneously. #R##N#Extensive experiments on real-world networks demonstrate that HICODE outperforms several state-of-the-art community detection methods in uncovering both the dominant and the hidden structure. In the Facebook university social networks, we find multiple non-redundant sets of communities that are strongly associated with residential hall, year of registration or career position of the faculties or students, while the state-of-the-art algorithms mainly locate the dominant ground truth category. In the Due to the difficulty of labeling all ground truth communities in real-world datasets, HICODE provides a promising approach to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Finding this unknown structure is an extremely important community detection problem.

Connected through hyperedge HE-1 (induced by source paper "hidden community detection on two layer stochastic models a theoretical prospective"):
  with members:
    - "revealing multiple layers of hidden community structure in networks"

Connected through hyperedge HE-2 (induced by source paper "single image reflection removal through cascaded refinement"):
  with members:
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "generative single image reflection separation"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "crrn multi scale guided concurrent reflection removal network"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "reflection separation using guided annotation"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-3 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks"):
  with members:
    - "gemsec graph embedding with self clustering"
    - "the ground truth about metadata and community detection in networks"
    - "community detection in networks with unequal groups"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "spectral redemption in clustering sparse networks"
    - "demon a local first discovery method for overlapping communities"
    - "percolation computation in complex networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "dynamo dynamic community detection by incrementally maximizing modularity"):
  with members:
    - "enhanced peerhunter detecting peer to peer botnets through network flow level community behavior analysis"
    - "graph convolutional neural networks for web scale recommender systems"
    - "community discovery in dynamic networks a survey"
    - "dyperm maximizing permanence for dynamic community detection"
    - "a real time detecting algorithm for tracking community structure of dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "etymo a new discovery engine for ai research"):
  with members:
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "krylov subspace approximation for local community detection in large networks"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-7 (induced by source paper "krylov subspace approximation for local community detection"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"
    - "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)