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: "spatial temporal relation networks for multi object tracking"
  Abstract: Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers. A good similarity score is expected to reflect multiple cues, e.g. appearance, location, and topology, over a long period of time. However, these cues are heterogeneous, making them hard to be combined in a unified network. As a result, existing methods usually encode them in separate networks or require a complex training approach. In this paper, we present a unified framework for similarity measurement which could simultaneously encode various cues and perform reasoning across both spatial and temporal domains. We also study the feature representation of a tracklet-object pair in depth, showing a proper design of the pair features can well empower the trackers. The resulting approach is named spatial-temporal relation networks (STRN). It runs in a feed-forward way and can be trained in an end-to-end manner. The state-of-the-art accuracy was achieved on all of the MOT15-17 benchmarks using public detection and online settings.

Connected through hyperedge HE-1 (induced by source paper "15 keypoints is all you need"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "learning temporal pose estimation from sparsely labeled videos"
    - "stand alone self attention in vision models"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "multi person articulated tracking with spatial and temporal embeddings"

Connected through hyperedge HE-2 (induced by source paper "learning a neural solver for multiple object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "relational inductive biases deep learning and graph networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"

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)