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: "learning to order things"
  Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of "search experts," each of which is a domain-specific query expansion strategy for a web search engine.

Connected through hyperedge HE-1 (induced by source paper "improving label ranking ensembles using boosting techniques"):
  with members:
    - "exploit bounding box annotations for multi label object recognition"

Connected through hyperedge HE-2 (induced by source paper "starnet targeted computation for object detection in point clouds"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "end to end multi view fusion for 3d object detection in lidar point clouds"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "ipod intensive point based object detector for point cloud"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-3 (induced by source paper "optimal bribery in voting"):
  with members:
    - "a unifying framework for manipulation problems"
    - "on the hardness of bribery variants in voting with cp nets"
    - "complexity of shift bribery in committee elections"
    - "prices matter for the parameterized complexity of shift bribery"
    - "frugal bribery in voting"
    - "complexity of manipulation bribery and campaign management in bucklin and fallback voting"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "approximation algorithms for campaign management"

Connected through hyperedge HE-4 (induced by source paper "local distance restricted bribery in voting"):
  with members:
    - "a unifying framework for manipulation problems"
    - "algorithms for destructive shift bribery"
    - "on the hardness of bribery variants in voting with cp nets"
    - "complexity of shift bribery in committee elections"
    - "prices matter for the parameterized complexity of shift bribery"
    - "frugal bribery in voting"
    - "complexity of manipulation bribery and campaign management in bucklin and fallback voting"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-5 (induced by source paper "multi task pairwise neural ranking for hashtag segmentation"):
  with members:
    - "microblog hashtag generation via encoding conversation contexts"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "exploring the limits of weakly supervised pretraining"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "tweet2vec character based distributed representations for social media"
    - "towards deep semantic analysis of hashtags"
    - "adam a method for stochastic optimization"
    - "empirical methods for compound splitting"

Connected through hyperedge HE-6 (induced by source paper "agent prioritization for autonomous navigation"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "multi context attention for human pose estimation"
    - "xgboost a scalable tree boosting system"
    - "learning to track at 100 fps with deep regression networks"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-7 (induced by source paper "learning choice functions"):
  with members:
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "self normalizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "approximating the least hypervolume contributor np hard in general but fast in practice"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-8 (induced by source paper "deep multi view learning to rank"):
  with members:
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "deep multimodal subspace clustering networks"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "a multi view embedding space for modeling internet images tags and their semantics"

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.LG(Machine Learning)