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: "multi camera multi object tracking"
  Abstract: In this paper, we propose a pipeline for multi-target visual tracking under multi-camera system. For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking. However, most of the multi-camera tracking algorithms emphasis on single camera across frame data association. Thus in our work, we model our tracking problem as a global graph, and adopt Generalized Maximum Multi Clique optimization problem as our core algorithm to take both across frame and across camera data correlation into account all together. Furthermore, in order to compute good similarity scores as the input of our graph model, we extract both appearance and dynamic motion similarities. For appearance feature, Local Maximal Occurrence Representation(LOMO) feature extraction algorithm for ReID is conducted. When it comes to capturing the dynamic information, we build Hankel matrix for each tracklet of target and apply rank estimation with Iterative Hankel Total Least Squares(IHTLS) algorithm to it. We evaluate our tracker on the challenging Terrace Sequences from EPFL CVLAB as well as recently published Duke MTMC dataset.

Connected through hyperedge HE-1 (induced by source paper "pedestrian models for autonomous driving part i low level models from sensing to tracking"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "posetrack joint multi person pose estimation and tracking"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "forecasting interactive dynamics of pedestrians with fictitious play"

Connected through hyperedge HE-2 (induced by source paper "state aware re identification feature for multi target multi camera tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "learning non uniform hypergraph for multi object tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "can composite appearance network and a novel evaluation metric for person tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "memory based online learning of deep representations from video streams"
    - "neural aggregation network for video face recognition"
    - "posetrack joint multi person pose estimation and tracking"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-4 (induced by source paper "multi target multi camera tracking by hierarchical clustering recent progress on dukemtmc project"):
  with members:
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "fast r cnn"

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)