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: "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
  Abstract: Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (this https URL) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.

Connected through hyperedge HE-1 (induced by source paper "attribute guided feature learning network for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "cross entropy adversarial view adaptation for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"

Connected through hyperedge HE-2 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning hybrid representation by robust dictionary learning in factorized compressed space"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "ram a region aware deep model for vehicle re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-3 (induced by source paper "joint discriminative and generative learning for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "adversarial open world person re identification"
    - "harmonious attention network for person re identification"
    - "multimodal unsupervised image to image translation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"

Connected through hyperedge HE-4 (induced by source paper "locality aware appearance metric for multi target multi camera tracking"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "improving online multiple object tracking with deep metric learning"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-5 (induced by source paper "eliminating cross camera bias for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"

Connected through hyperedge HE-6 (induced by source paper "simulating content consistent vehicle datasets with attribute descent"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "moment matching for multi source domain adaptation"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to simulate"
    - "unity a general platform for intelligent agents"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-7 (induced by source paper "vehicle re identification an efficient baseline using triplet embedding"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-8 (induced by source paper "exploit the connectivity multi object tracking with trackletnet"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "features for multi target multi camera tracking and re identification"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "re ranking person re identification with k reciprocal encoding"
    - "fusion of head and full body detectors for multi object tracking"

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)