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: "graph regularized non negative matrix factorization by maximizing correntropy"
  Abstract: Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are $l_2$ distance or Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels, such as correntropy, are proposed. However, the current correntropy-based NMF only targets on the low-level features without considering the intrinsic geometrical distribution of data. In this paper, we propose a new NMF algorithm that preserves local invariance by adding graph regularization into the process of max-correntropy-based matrix factorization. Meanwhile, each feature can learn corresponding kernel from the data. The experiment results of Caltech101 and Caltech256 show the benefits of such combination against other NMF algorithms for the unsupervised image clustering.

Connected through hyperedge HE-1 (induced by source paper "regularized maximum correntropy machine"):
  with members:
    - "a characterization of cybersecurity posture from network telescope data"
    - "semantic sparse recoding of visual content for image applications"
    - "document clustering based on max correntropy non negative matrix factorization"
    - "maximum mutual information regularized classification"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "latent semantic learning with structured sparse representation for human action recognition"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-2 (induced by source paper "maximum mutual information regularized classification"):
  with members:
    - "document clustering based on max correntropy non negative matrix factorization"
    - "an evasion and counter evasion study in malicious websites detection"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-3 (induced by source paper "structure preserving large imagery reconstruction"):
  with members:
    - "document clustering based on max correntropy non negative matrix factorization"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"

Connected through hyperedge HE-4 (induced by source paper "learning manifold to regularize nonnegative matrix factorization"):
  with members:
    - "location aided energy balancing strategy in green cellular networks"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"

Connected through hyperedge HE-5 (induced by source paper "document clustering based on max correntropy non negative matrix factorization"):
  with members:
    - "distributed spectrum aware clustering in cognitive radio sensor networks"
    - "adaptive learning of region based plsa model for total scene annotation"

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)