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: "axioms for graph clustering quality functions"
  Abstract: We investigate properties that intuitively ought to be satisfied by graph clustering quality functions, that is, functions that assign a score to a clustering of a graph. Graph clustering, also known as network community detection, is often performed by optimizing such a function. Two axioms tailored for graph clustering quality functions are introduced, and the four axioms introduced in previous work on distance based clustering are reformulated and generalized for the graph setting. We show that modularity, a standard quality function for graph clustering, does not satisfy all of these six properties. This motivates the derivation of a new family of quality functions, adaptive scale modularity, which does satisfy the proposed axioms. Adaptive scale modularity has two parameters, which give greater flexibility in the kinds of clusterings that can be found. Standard graph clustering quality functions, such as normalized cut and unnormalized cut, are obtained as special cases of adaptive scale modularity. #R##N#In general, the results of our investigation indicate that the considered axiomatic framework covers existing `good' quality functions for graph clustering, and can be used to derive an interesting new family of quality functions.

Connected through hyperedge HE-1 (induced by source paper "graph clustering via qubo and digital annealing"):
  with members:
    - "using synthetic networks for parameter tuning in community detection"
    - "a statistical density based analysis of graph clustering algorithm performance"
    - "testing graph clusterability algorithms and lower bounds"
    - "a tutorial on formulating and using qubo models"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance"):
  with members:
    - "using synthetic networks for parameter tuning in community detection"
    - "size matters a comparative analysis of community detection algorithms"
    - "graph sketching based massive data clustering"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "extension of modularity density for overlapping community structure"
    - "defining and evaluating network communities based on ground truth"
    - "bad communities with high modularity"

Connected through hyperedge HE-3 (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"
    - "metrics for community analysis a survey"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-4 (induced by source paper "systematic analysis of cluster similarity indices towards bias free cluster validation"):
  with members:
    - "a brief survey of text mining classification clustering and extraction techniques"

Connected through hyperedge HE-5 (induced by source paper "morse theory and an impossibility theorem for graph clustering"):
  with members:
    - "structure and inference in annotated networks"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-6 (induced by source paper "on the discrepancy between kleinberg s clustering axioms and k means clustering algorithm behavior"):
  with members:
    - "incremental clustering the case for extra clusters"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-7 (induced by source paper "metrics for community analysis a survey"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "higher order organization of complex networks"
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "z score based modularity for community detection in networks"
    - "permanence and community structure in complex networks"
    - "generalized measures for the evaluation of community detection methods"
    - "generalization of clustering agreements and distances for overlapping clusters and network communities"

Connected through hyperedge HE-8 (induced by source paper "detecting communities is hard and counting them is even harder"):
  with members:
    - "community recovery in graphs with locality"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "how robust are reconstruction thresholds for community detection"
    - "eth hardness for signaling in symmetric zero sum games"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "am with multiple merlins"
    - "finding endogenously formed communities"

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.CV(Computer Vision and Pattern Recognition)