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: "metrics for community analysis a survey"
  Abstract: Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms - disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

Connected through hyperedge HE-1 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"
    - "submodular inference of diffusion networks from multiple trees"

Connected through hyperedge HE-2 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "how did the discussion go discourse act classification in social media conversations"):
  with members:
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "distributed representations of sentences and documents"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-4 (induced by source paper "overlapping community detection in networks based on link partitioning and partitioning around medoids"):
  with members:
    - "modular networks for validating community detection algorithms"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "axioms for graph clustering quality functions"

Connected through hyperedge HE-5 (induced by source paper "community detection through likelihood optimization in search of a sound model"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "network structure exploration via bayesian nonparametric models"
    - "clustering and community detection in directed networks a survey"
    - "model of complex networks based on citation dynamics"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "community structure a comparative evaluation of community detection methods"):
  with members:
    - "an empirical characterization of community structures in complex networks using a bivariate map of quality metrics"
    - "efficient method for estimating the number of communities in a network"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "z score based modularity for community detection in networks"
    - "mixing local and global information for community detection in large networks"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-7 (induced by source paper "ensemble based overlapping community detection using disjoint community structures"):
  with members:
    - "dyperm maximizing permanence for dynamic community detection"
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "on the formation of circles in co authorship networks"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "discovering social circles in ego networks"

Connected through hyperedge HE-8 (induced by source paper "detecting statistically significant communities"):
  with members:
    - "network structure from rich but noisy data"
    - "prioritizing network communities"
    - "z score based modularity for community detection in networks"
    - "significance based community detection in weighted networks"
    - "a testing based extraction algorithm for identifying significant communities in networks"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)