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: "development of a n type gm phd filter for multiple target multiple type visual tracking"
  Abstract: We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having $N\geq2$ different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter. Under Gaussianity and linearity assumptions, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors' information into this filter for two scenarios. For both cases, Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment metric and discrimination rate. This shows the improved performance of our strategy on real video sequences.

Connected through hyperedge HE-1 (induced by source paper "occlusion robust online multi object visual tracking using a gm phd filter with a cnn based re identification"):
  with members:
    - "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "region based quality estimation network for large scale person re identification"
    - "person transfer gan to bridge domain gap for person re identification"

Connected through hyperedge HE-2 (induced by source paper "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "focal loss for dense object detection"
    - "region based quality estimation network for large scale person re identification"
    - "person transfer gan to bridge domain gap for person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "a fast multi object tracking system using an object detector ensemble"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "light head r cnn in defense of two stage object detector"
    - "mot16 a benchmark for multi object tracking"
    - "simple online and realtime tracking"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "performance measures and a data set for multi target multi camera tracking"
    - "multi person tracking by multicut and deep matching"
    - "you only look once unified real time object detection"

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)