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: "local network community detection with continuous optimization of conductance and weighted kernel k means"
  Abstract: Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way. Conductance is a popular objective function used in many algorithms for local community detection. This paper studies a continuous relaxation of conductance. We show that continuous optimization of this objective still leads to discrete communities. We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance. Conductance is obtained by setting $\sigma$ to $0$. Two algorithms, EMc and PGDc, are proposed to locally optimize $\sigma$-conductance and automatically tune the parameter $\sigma$. They are based on expectation maximization and projected gradient descent, respectively. We prove locality and give performance guarantees for EMc and PGDc for a class of dense and well separated communities centered around the seeds. Experiments are conducted on networks with ground-truth communities, comparing to state-of-the-art graph diffusion algorithms for conductance optimization. On large graphs, results indicate that EMc and PGDc stay localized and produce communities most similar to the ground, while graph diffusion algorithms generate large communities of lower quality.

Connected through hyperedge HE-1 (induced by source paper "correlation based community detection"):
  with members:
    - "higher order organization of complex networks"
    - "permanence and community structure in complex networks"
    - "heat kernel based community detection"
    - "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"
    - "multi level algorithms for modularity clustering"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"

Connected through hyperedge HE-2 (induced by source paper "a survey on theoretical advances of community detection in networks"):
  with members:
    - "structure and inference in annotated networks"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "spectra of random graphs with arbitrary expected degrees"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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)