human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the cyborg astrobiologist testing a novelty detection algorithm on two mobile exploration systems at rivas vaciamadrid in spain and at the mars desert research station in utah"):
  Abstract: In previous work, a platform was developed for testing computer-vision algorithms for robotic planetary exploration. This platform consisted of a digital video camera connected to a wearable computer for real-time processing of images at geological and astrobiological field sites. The real-time processing included image segmentation and the generation of interest points based upon uncommonness in the segmentation maps. Also in previous work, this platform for testing computer-vision algorithms has been ported to a more ergonomic alternative platform, consisting of a phone camera connected via the Global System for Mobile Communications (GSM) network to a remote-server computer. The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon colour, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing Computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone camera connected to a netbook Computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed Lis to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colours to test this algorithm. The algorithm robustly recognized previously observed units by their colour, while requiring only a single image or a few images to learn colours as familiar, demonstrating its fast learning capability.

Includes members:
  Member "the cyborg astrobiologist porting from a wearable computer to the astrobiology phone cam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the cyborg astrobiologist matching of prior textures by image compression for geological mapping and novelty detection")
  Member "the cyborg astrobiologist scouting red beds for uncommon features with geological significance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the cyborg astrobiologist matching of prior textures by image compression for geological mapping and novelty detection")
  Member "the cyborg astrobiologist first field experience":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the cyborg astrobiologist matching of prior textures by image compression for geological mapping and novelty detection")
  Member "cyborg systems as platforms for computer vision algorithm development for astrobiology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "field geology with a wearable computer 1st results of the cyborg astrobiologist system")
  Member "multi modal human machine communication for instructing robot grasping tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "methodologies for realizing natural language facilitated human robot cooperation a review")

Question: Please tell me which class the source paper 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)