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: "demon a local first discovery method for overlapping communities"
  Abstract: Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.

Connected through hyperedge HE-1 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks"):
  with members:
    - "gemsec graph embedding with self clustering"
    - "hidden community detection in social networks"
    - "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"
    - "percolation computation in complex networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "discovering communities of community discovery"):
  with members:
    - "a survey of community search over big graphs"
    - "community structure a comparative evaluation of community detection methods"
    - "community discovery in dynamic networks a survey"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"
    - "a classification for community discovery methods in complex networks"
    - "community detection via semi synchronous label propagation algorithms"

Connected through hyperedge HE-3 (induced by source paper "debiasing community detection the importance of lowly connected nodes"):
  with members:
    - "characterizing the 2016 russian ira influence campaign"
    - "latent space model for multi modal social data"
    - "clustering and community detection in directed networks a survey"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "community detection in networks with node attributes"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "a customisable pipeline for continuously harvesting socially minded twitter users"):
  with members:
    - "recruiting from the network discovering twitter users who can help combat zika epidemics"
    - "the perceived assortativity of social networks methodological problems and solutions"
    - "measuring user influence on twitter"
    - "semantifying twitter the influencetracker ontology"

Connected through hyperedge HE-5 (induced by source paper "uncovering complex overlapping pattern of communities in large scale social networks"):
  with members:
    - "community detection algorithm evaluation with ground truth data"
    - "the ground truth about metadata and community detection in networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "efficient detection of communities with significant overlaps in networks partial community merger algorithm"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "graph spectra and the detectability of community structure in networks"
    - "core periphery structure in networks"

Connected through hyperedge HE-6 (induced by source paper "label propagation for clustering"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "the many facets of community detection in complex networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
    - "group detection in complex networks an algorithm and comparison of the state of the art"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "towards linear time overlapping community detection in social networks"

Connected through hyperedge HE-7 (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"
    - "hidden community detection 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"

Connected through hyperedge HE-8 (induced by source paper "krylov subspace approximation for local community detection"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "hidden community detection 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"
    - "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.DS(Data Structures and Algorithms)