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: "adaptive evolutionary clustering"
  Abstract: In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.

Connected through hyperedge HE-1 (induced by source paper "lstm assisted evolutionary self expressive subspace clustering"):
  with members:
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "evolutionary self expressive models for subspace clustering"
    - "accelerated sparse subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "consensus of hybrid multi agent systems"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "a literature survey of benchmark functions for global optimisation problems"

Connected through hyperedge HE-3 (induced by source paper "online collection and forecasting of resource utilization in large scale distributed systems"):
  with members:
    - "compressed sensing in multi hop large scale wireless sensor networks based on routing topology tomography"

Connected through hyperedge HE-4 (induced by source paper "evolutionary self expressive models for subspace clustering"):
  with members:
    - "evolutionary clustering via message passing"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"

Connected through hyperedge HE-5 (induced by source paper "multi layer graph analysis for dynamic social networks"):
  with members:
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "multi criteria anomaly detection using pareto depth analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "dynamic stochastic blockmodels for time evolving social networks"):
  with members:
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-7 (induced by source paper "identification of dynamic functional brain network states through tensor decomposition"):
  with members:
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "dynamic clustering via asymptotics of the dependent dirichlet process mixture"):
  with members:
    - "revisiting k means new algorithms via bayesian nonparametrics"

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.LG(Machine Learning)