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: "classes for fast maximum entropy training"
  Abstract: Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a novel speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer non-zero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling.

Connected through hyperedge HE-1 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning"):
  with members:
    - "effective medical test suggestions using deep reinforcement learning"
    - "classification with costly features as a sequential decision making problem"
    - "discretizing continuous action space for on policy optimization"
    - "graph neural networks a review of methods and applications"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"

Connected through hyperedge HE-2 (induced by source paper "the two pass softmax algorithm"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "high performance ultra low precision convolutions on mobile devices"
    - "efficient softmax approximation for gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-3 (induced by source paper "define deep factorized input word embeddings for neural sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-4 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "making better mistakes leveraging class hierarchies with deep networks"):
  with members:
    - "integrating domain knowledge using hierarchies to improve deep classifiers"
    - "when does label smoothing help"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "class balanced loss based on effective number of samples"
    - "theoretically principled trade off between robustness and accuracy"
    - "on the convergence of adam and beyond"
    - "do convolutional neural networks learn class hierarchy"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "superbloom bloom filter meets transformer"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "next item recommendation with self attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hash embeddings for efficient word representations"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-7 (induced by source paper "doubly sparse sparse mixture of sparse experts for efficient softmax inference"):
  with members:
    - "learning to screen for fast softmax inference on large vocabulary neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "efficient softmax approximation for gpus"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-8 (induced by source paper "graph embeddings at scale"):
  with members:
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "node2vec scalable feature learning for networks"
    - "e commerce in your inbox product recommendations at scale"
    - "deepwalk online learning of social representations"

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)