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: "maximizing diversity in biology and beyond"
  Abstract: Entropy, under a variety of names, has long been used as a measure of diversity in ecology, as well as in genetics, economics and other fields. There is a spectrum of viewpoints on diversity, indexed by a real parameter q giving greater or lesser importance to rare species. Leinster and Cobbold (2012) proposed a one-parameter family of diversity measures taking into account both this variation and the varying similarities between species. Because of this latter feature, diversity is not maximized by the uniform distribution on species. So it is natural to ask: which distributions maximize diversity, and what is its maximum value? In principle, both answers depend on q, but our main theorem is that neither does. Thus, there is a single distribution that maximizes diversity from all viewpoints simultaneously, and any list of species has an unambiguous maximum diversity value. Furthermore, the maximizing distribution(s) can be computed in finite time, and any distribution maximizing diversity from some particular viewpoint                                        q               >               0                                  actually maximizes diversity for all q. Although we phrase our results in ecological terms, they apply very widely, with applications in graph theory and metric geometry.

Connected through hyperedge HE-1 (induced by source paper "gear geometry aware renyi information"):
  with members:
    - "stochastic wasserstein barycenters"
    - "improving gans using optimal transport"
    - "fixing a broken elbo"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improved techniques for training gans"
    - "taming the noise in reinforcement learning via soft updates"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "natively interpretable machine learning and artificial intelligence preliminary results and future directions"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning and the information bottleneck principle"
    - "falling rule lists"

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.IT(Information Theory)