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: "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
  Abstract: The world's collective knowledge is evolving through research and new scientific discoveries. It is becoming increasingly difficult to objectively rank the impact research institutes have on global advancements. However, since the funding, governmental support, staff and students quality all mirror the projected quality of the institution, it becomes essential to measure the affiliation's rating in a transparent and widely accepted way. We propose and investigate several methods to rank affiliations based on the number of their accepted papers at future academic conferences. We carry out our investigation using publicly available datasets such as the Microsoft Academic Graph, a heterogeneous graph which contains various information about academic papers. We analyze several models, starting with a simple probabilities-based method and then gradually expand our training dataset, engineer many more features and use mixed models and gradient boosted decision trees models to improve our predictions.

Connected through hyperedge HE-1 (induced by source paper "locally constant networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "on the convergence of adam and beyond"
    - "moleculenet a benchmark for molecular machine learning"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "efficient non greedy optimization of decision trees"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-2 (induced by source paper "securegbm secure multi party gradient boosting"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "gossip learning with linear models on fully distributed data"

Connected through hyperedge HE-3 (induced by source paper "adaptive neural trees"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "continuously constructive deep neural networks"
    - "ndt neual decision tree towards fully functioned neural graph"
    - "large scale evolution of image classifiers"
    - "lifelong learning with dynamically expandable networks"
    - "efficient architecture search by network transformation"
    - "energy efficient amortized inference with cascaded deep classifiers"
    - "efficient multitask feature and relationship learning"

Connected through hyperedge HE-4 (induced by source paper "the coverage of microsoft academic analyzing the publication output of a university"):
  with members:
    - "leveraging citation networks to visualize scholarly influence over time"
    - "citation analysis with microsoft academic"
    - "semantometrics towards fulltext based research evaluation"
    - "quantifying the diaspora of knowledge in the last century"
    - "ranking academic institutions on potential paper acceptance in upcoming conferences"
    - "ensemble enabled weighted pagerank"
    - "simplified relative citation ratio for static paper ranking ufmg latin at wsdm cup 2016"
    - "static ranking of scholarly papers using article level eigenfactor alef"

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)