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 the curriculum with bayesian optimization for task specific word representation learning"
  Abstract: We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus. We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order.

Connected through hyperedge HE-1 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-2 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"

Connected through hyperedge HE-3 (induced by source paper "autosem automatic task selection and mixing in multi task learning"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "dynamic multi level multi task learning for sentence simplification"
    - "automated curriculum learning for neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"

Connected through hyperedge HE-6 (induced by source paper "learning a multitask curriculum for neural machine translation"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning based curriculum optimization for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "reinforced co training"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "automated curriculum learning for neural networks"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "neural machine translation training in a multi domain scenario"

Connected through hyperedge HE-8 (induced by source paper "carpe diem seize the samples uncertain at the moment for adaptive batch selection"):
  with members:
    - "submodular batch selection for training deep neural networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "training region based object detectors with online hard example mining"
    - "online batch selection for faster training of neural networks"

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)