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: "anchor tasks inexpensive shared and aligned tasks for domain adaptation"
  Abstract: We introduce a novel domain adaptation formulation from synthetic dataset (source domain) to real dataset (target domain) for the category of tasks with per-pixel predictions. The annotations of these tasks are relatively hard to acquire in the real world, such as single-view depth estimation or surface normal estimation. Our key idea is to introduce anchor tasks, whose annotations are (1) less expensive to acquire than the main task, such as facial landmarks and semantic segmentations; and (2) shared in availability for both synthetic and real datasets so that it serves as "anchor" between tasks; and finally (3) aligned spatially with main task annotations on a per-pixel basis so that it also serves as spatial anchor between tasks' outputs. To further utilize spatial alignment between the anchor and main tasks, we introduce a novel freeze approach that freezes the final layers of our network after training on the source domain so that spatial and contextual relationship between tasks are maintained when adapting on the target domain. We evaluate our methods on two pairs of datasets, performing surface normal estimation in indoor scenes and faces, using semantic segmentation and facial landmarks as anchor tasks separately. We show the importance of using anchor tasks in both synthetic and real domains, and that the freeze approach outperforms competing approaches, reaching results in facial images on par with the state-of-the-art system that leverages detailed facial appearance model.

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)