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: "feature encoding in band limited distributed surveillance systems"
  Abstract: Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the visual features in order to reduce their dimensionality and thus save the network bandwidth in distributed wireless smart camera networks. We demonstrate the effectiveness of the proposed approach through extensive experiments on two surveillance recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "person re identification using visual attention"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "spatial random sampling a structure preserving data sketching tool"
    - "person re identification by deep joint learning of multi loss classification"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"

Connected through hyperedge HE-2 (induced by source paper "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"):
  with members:
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "innovation pursuit a new approach to subspace clustering"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparse modeling for image and vision processing"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"

Connected through hyperedge HE-3 (induced by source paper "multi view task driven recognition in visual sensor networks"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "control allocation for wide area coordinated damping"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "quality based multimodal classification using tree structured sparsity"

Connected through hyperedge HE-4 (induced by source paper "end to end binary representation learning via direct binary embedding"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "deep convolutional ranking for multilabel image 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)