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: "integrating graph partitioning and matching for trajectory analysis in video surveillance"
  Abstract: In order to track moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g., 15 frames. We initially calculate a foreground map at each frame obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching. The task can be formulated by maximizing a posteriori under the Bayesian framework, in which we integrate the spatio-temporal contexts and the appearance models. The probabilistic inference is achieved by a data-driven Markov chain Monte Carlo algorithm. Given a period of observed frames, the algorithm simulates an ergodic and aperiodic Markov chain, and it visits a sequence of solution states in the joint space of spatial graph partitioning and temporal graph matching. In the experiments, our method is tested on several challenging videos from the public datasets of visual surveillance, and it outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "improving description based person re identification by multi granularity image text alignments"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "stacked cross attention for image text matching"
    - "pamm pose aware multi shot matching for improving person re identification"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "convolutional image captioning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"

Connected through hyperedge HE-2 (induced by source paper "a survey on trajectory clustering analysis"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "globally continuous and non markovian crowd activity analysis from videos"
    - "a robust and efficient video representation for action recognition"
    - "learning to segment moving objects in videos"
    - "a gesture recognition system for detecting behavioral patterns of adhd"
    - "classifying and visualizing motion capture sequences using deep neural networks"
    - "ganc greedy agglomerative normalized cut"
    - "discovery of convoys in trajectory databases"

Connected through hyperedge HE-3 (induced by source paper "complex background subtraction by pursuing dynamic spatio temporal models"):
  with members:
    - "learning latent spatio temporal compositional model for human action recognition"
    - "background modeling using adaptive pixelwise kernel variances in a hybrid feature space"
    - "robust principal component analysis"

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)