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: "general optimization technique for high quality community detection in complex networks"
  Abstract: Recent years have witnessed the development of a large body of algorithms for community detection in complex networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function; for description length, its performance is on par with the original Infomap algorithm. The execution time of our algorithm is on par with non-greedy alternatives present in literature, and networks of up to 10,000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks which require the quality of partitioning to be as high as possible, and are not limited by strict time constraints. Finally, based on the most effective of the available optimization techniques, we compare the performance of modularity and code length as objective functions, in terms of the quality of the partitions one can achieve by optimizing them. To this end, we evaluated the ability of each objective function to reconstruct the underlying structure of a large set of synthetic and real-world networks.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection"):
  with members:
    - "efficient gan based anomaly detection"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cnn features are also great at unsupervised classification"
    - "learning representations for automatic colorization"
    - "global multi layer network of human mobility"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-2 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach"):
  with members:
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "social structure of facebook networks"

Connected through hyperedge HE-3 (induced by source paper "iedc an integrated approach for overlapping and non overlapping community detection"):
  with members:
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "relevant based structure learning for feature selection"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "social structure of facebook networks"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "visual analytics for loan guarantee network risk management"):
  with members:
    - "anomaly detection in online social networks"
    - "xgboost a scalable tree boosting system"
    - "technical report graph structured sparse optimization for connected subgraph detection"
    - "sales pipeline win propensity prediction a regression approach"
    - "the lifecycle and cascade of wechat social messaging groups"

Connected through hyperedge HE-5 (induced by source paper "global multi layer network of human mobility"):
  with members:
    - "scaling of foreign attractiveness for countries and states"
    - "yfcc100m the new data in multimedia research"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-6 (induced by source paper "tweeting over the border an empirical study of transnational migration in san diego and tijuana"):
  with members:
    - "geo located twitter as proxy for global mobility patterns"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "using location based social networks to validate human mobility and relationships models"

Connected through hyperedge HE-7 (induced by source paper "geo located twitter as proxy for global mobility patterns"):
  with members:
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-8 (induced by source paper "the impact of social segregation on human mobility in developing and urbanized regions"):
  with members:
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"
    - "data for development the d4d challenge on mobile phone data"
    - "objective criteria for the evaluation of clustering methods"

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)