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: "adaptive objectness for object tracking"
  Abstract: To exploit the reliable prior knowledge that the target object in tracking must be an  object  other than nonobject, in this letter, we propose to adapt objectness for visual object tracking. Instead of directly applying an existing objectness measure that is generic and handles various objects and environments, we adapt it to be compatible to the specific tracking sequence and object. More specifically, we use the newly proposed binarized normed gradient (BING) objectness as the base, and then train an object-adaptive objectness for each tracking task. The training is implemented by using an adaptive support vector machine that integrates information from the specific tracking target into the BING measure. We emphasize that the benefit of the proposed adaptive objectness, named ADOBING, is generic. To show this, we combine ADOBING with eight top performed trackers in recent evaluations. We run the ADOBING-enhanced trackers along with their base trackers on the CVPR2013 benchmark, and our methods consistently improve the base trackers both in overall performance and under all challenge factors. Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.

Connected through hyperedge HE-1 (induced by source paper "beyond local search tracking objects everywhere with instance specific proposals"):
  with members:
    - "transferring rich feature hierarchies for robust visual tracking"
    - "understanding and diagnosing visual tracking systems"
    - "high speed tracking with kernelized correlation filters"
    - "how good are detection proposals really"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "tracking randomly moving objects on edge box proposals"):
  with members:
    - "transferring rich feature hierarchies for robust visual tracking"
    - "understanding and diagnosing visual tracking systems"
    - "how good are detection proposals really"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "coreset based adaptive tracking"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "relative errors for deterministic low rank matrix approximations"
    - "simple and deterministic matrix sketching"

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)