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: "decorrelation of neutral vector variables theory and applications"
  Abstract: In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations.

Connected through hyperedge HE-1 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"
    - "a large deviations approach to secure lossy compression"

Connected through hyperedge HE-2 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-3 (induced by source paper "ontology based global and collective motion patterns for event classification in basketball videos"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "switching convolutional neural network for crowd counting"
    - "spatiotemporal modeling for crowd counting in videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"
    - "a hierarchical deep temporal model for group activity recognition"
    - "detecting events and key actors in multi person videos"

Connected through hyperedge HE-4 (induced by source paper "channel max pooling layer for fine grained vehicle classification"):
  with members:
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "the role of data analysis in the development of intelligent energy networks"
    - "low rank bilinear pooling for fine grained classification"
    - "sgdr stochastic gradient descent with restarts"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-5 (induced by source paper "recsys dan discriminative adversarial networks for cross domain recommender systems"):
  with members:
    - "lrmm learning to recommend with missing modalities"
    - "neural collaborative filtering"
    - "adversarial discriminative domain adaptation"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "vehicular edge computing via deep reinforcement learning"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "optimal auction for edge computing resource management in mobile blockchain networks a deep learning approach"
    - "the role of data analysis in the development of intelligent energy networks"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-7 (induced by source paper "mobile big data analysis with machine learning"):
  with members:
    - "distributed optimization using the primal dual method of multipliers"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "the role of data analysis in the development of intelligent energy networks"
    - "big data caching for networking moving from cloud to edge"
    - "mobile big data analytics using deep learning and apache spark"
    - "soft defined heterogeneous vehicular network architecture and challenges"
    - "5g wireless backhaul networks challenges and research advances"

Connected through hyperedge HE-8 (induced by source paper "fast matrix factorization with non uniform weights on missing data"):
  with members:
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "adversarial personalized ranking for recommendation"
    - "attributed social network embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

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)