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: "hash embeddings for efficient word representations"
  Abstract: We present hash embeddings, an efficient method for representing words in a continuous vector form. A hash embedding may be seen as an interpolation between a standard word embedding and a word embedding created using a random hash function (the hashing trick). In hash embeddings each token is represented by $k$ $d$-dimensional embeddings vectors and one $k$ dimensional weight vector. The final $d$ dimensional representation of the token is the product of the two. Rather than fitting the embedding vectors for each token these are selected by the hashing trick from a shared pool of $B$ embedding vectors. Our experiments show that hash embeddings can easily deal with huge vocabularies consisting of millions of tokens. When using a hash embedding there is no need to create a dictionary before training nor to perform any kind of vocabulary pruning after training. We show that models trained using hash embeddings exhibit at least the same level of performance as models trained using regular embeddings across a wide range of tasks. Furthermore, the number of parameters needed by such an embedding is only a fraction of what is required by a regular embedding. Since standard embeddings and embeddings constructed using the hashing trick are actually just special cases of a hash embedding, hash embeddings can be considered an extension and improvement over the existing regular embedding types.

Connected through hyperedge HE-1 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-3 (induced by source paper "smaller text classifiers with discriminative cluster embeddings"):
  with members:
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "natural language processing with small feed forward networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "character level convolutional networks for text classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "compositional coding for collaborative filtering"):
  with members:
    - "discrete factorization machines for fast feature based recommendation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "collaborative filtering with social local models"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"
    - "neural attentive session based recommendation"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "apk2vec semi supervised multi view representation learning for profiling android applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "anonymous walk embeddings"
    - "struc2vec learning node representations from structural identity"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "deep graph attention model"
    - "graph2vec learning distributed representations of graphs"
    - "contextual weisfeiler lehman graph kernel for malware detection"
    - "node2vec scalable feature learning for 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)