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 "a unified formulation for visual odometry"):
  Abstract: Monocular Odometry systems can be broadly categorized as being either Direct, Indirect, or a hybrid of both. While Indirect systems process an alternative image representation to compute geometric residuals, Direct methods process the image pixels directly to generate photometric residuals. Both paradigms have distinct but often complementary properties. This paper presents a Unified Formulation for Visual Odometry, referred to as UFVO, with the following key contributions: (1) a tight coupling of photometric (Direct) and geometric (Indirect) measurements using a joint multi-objective optimization, (2) the use of a utility function as a decision maker that incorporates prior knowledge on both paradigms, (3) descriptor sharing, where a feature can have more than one type of descriptor and its different descriptors are used for tracking and mapping, (4) the depth estimation of both corner features and pixel features within the same map using an inverse depth parametrization, and (5) a corner and pixel selection strategy that extracts both types of information, while promoting a uniform distribution over the image domain. Experiments show that our proposed system can handle large inter-frame motions, inherits the sub-pixel accuracy of direct methods, can run efficiently in real-time, can generate an Indirect map representation at a marginal computational cost when compared to traditional Indirect systems, all while outperforming state of the art in Direct, Indirect and hybrid systems.

Includes members:
  Member "loosely coupled semi direct monocular slam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "condition invariant multi view place recognition")
  Member "fdmo feature assisted direct monocular odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature based visual odometry prior for real time semi dense stereo slam")
  Member "ldso direct sparse odometry with loop closure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "keyframe based monocular slam":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play slam a unified slam architecture for modularity and ease of use")
  Member "direct sparse odometry":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "a photometrically calibrated benchmark for monocular visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast loop closure detection via binary content")
  Member "orb slam a versatile and accurate monocular slam system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

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)