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: "collaborative annotation of semantic objects in images with multi granularity supervisions"
  Abstract: Per-pixel masks of semantic objects are very useful in many applications, which, however, are tedious to be annotated. In this paper, we propose a human-agent collaborative annotation approach that can efficiently generate per-pixel masks of semantic objects in tagged images with multi-granularity supervisions. Given a set of tagged image, a computer agent is first dynamically generated to roughly localize the semantic objects described by the tag. The agent first extracts massive object proposals from an image and then infer the tag-related ones under the weak and strong supervisions from linguistically and visually similar images and previously annotated object masks. By representing such supervisions by over-complete dictionaries, the tag-related object proposals can pop-out according to their sparse coding length, which are then converted to superpixels with binary labels. After that, human annotators participate in the annotation process by flipping labels and dividing superpixels with mouse clicks, which are used as click supervisions that teach the agent to recover false positives/negatives in processing images with the same tags. Experimental results show that our approach can facilitate the annotation process and generate object masks that are highly consistent with those generated by the LabelMe toolbox.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "neural odes for image segmentation with level sets"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"
    - "waveglow a flow based generative network for speech synthesis"
    - "group normalization"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "neural ordinary differential equations"
    - "squeeze and excitation networks"
    - "rethinking atrous convolution for semantic image segmentation"

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)