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: "adversarial contrastive estimation"
  Abstract: Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.

Connected through hyperedge HE-1 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-2 (induced by source paper "regularized adversarial sampling and deep time aware attention for click through rate prediction"):
  with members:
    - "adversarial sampling and training for semi supervised information retrieval"
    - "telepath understanding users from a human vision perspective in large scale recommender systems"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "attention is all you need"
    - "deep cross network for ad click predictions"
    - "optimizing gross merchandise volume via dnn mab dynamic ranking paradigm"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"

Connected through hyperedge HE-3 (induced by source paper "compositional fairness constraints for graph embeddings"):
  with members:
    - "learning adversarially fair and transferable representations"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "representation learning on graphs methods and applications"
    - "on formalizing fairness in prediction with machine learning"
    - "new fairness metrics for recommendation that embrace differences"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "learning determinantal point processes by corrective negative sampling"):
  with members:
    - "improving negative sampling for word representation using self embedded features"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "training region based object detectors with online hard example mining"
    - "kronecker determinantal point processes"
    - "fixed point algorithms for learning determinantal point processes"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

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.CL(Computation and Language)