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 "3d scene grammar for parsing rgb d pointclouds"):
  Abstract: We pose 3D scene-understanding as a problem of parsing in a grammar. A grammar helps us capture the compositional structure of real-word objects, e.g., a chair is composed of a seat, a back-rest and some legs. Having multiple rules for an object helps us capture structural variations in objects, e.g., a chair can optionally also have arm-rests. Finally, having rules to capture composition at different levels helps us formulate the entire scene-processing pipeline as a single problem of finding most likely parse-tree---small segments combine to form parts of objects, parts to objects and objects to a scene. We attach a generative probability model to our grammar by having a feature-dependent probability function for every rule. We evaluated it by extracting labels for every segment and comparing the results with the state-of-the-art segment-labeling algorithm. Our algorithm was outperformed by the state-or-the-art method. But, Our model can be trained very efficiently (within seconds), and it scales only linearly in with the number of rules in the grammar. Also, we think that this is an important problem for the 3D vision community. So, we are releasing our dataset and related code.

Includes members:
  Member "unstructured human activity detection from rgbd images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "human action recognition and assessment via deep neural network self organization")
  Member "contextually guided semantic labeling and search for 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "segcloud semantic segmentation of 3d point clouds")
  Member "the generalized a architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "output space search for structured prediction")

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)