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: "anomaly detection in unstructured environments using bayesian nonparametric scene modeling"
  Abstract: This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. In the second dataset, consisting of video data from a static seafloor camera capturing images of a busy coral reef, the proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.

Connected through hyperedge HE-1 (induced by source paper "active reward learning for co robotic vision based exploration in bandwidth limited environments"):
  with members:
    - "information guided robotic maximum seek and sample in partially observable continuous environments"
    - "streaming scene maps for co robotic exploration in bandwidth limited environments"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "an approach to autonomous science by modeling geological knowledge in a bayesian framework"
    - "feature discovery and visualization of robot mission data using convolutional autoencoders and bayesian nonparametric topic models"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "streaming scene maps for co robotic exploration in bandwidth limited environments"):
  with members:
    - "an approach to autonomous science by modeling geological knowledge in a bayesian framework"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "gibbs sampling strategies for semantic perception of streaming video data"

Connected through hyperedge HE-3 (induced by source paper "feature discovery and visualization of robot mission data using convolutional autoencoders and bayesian nonparametric topic models"):
  with members:
    - "gibbs sampling strategies for semantic perception of streaming video data"

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)