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: "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
  Abstract: Embedding-based methods for knowledge base completion (KBC) learn representations of entities and relations in a vector space, along with the scoring function to estimate the likelihood of relations between entities. The learnable class of scoring functions is designed to be expressive enough to cover a variety of real-world relations, but this expressive comes at the cost of an increased number of parameters. In particular, parameters in these methods are superfluous for relations that are either symmetric or antisymmetric. To mitigate this problem, we propose a new L1 regularizer for Complex Embeddings, which is one of the state-of-the-art embedding-based methods for KBC. This regularizer promotes symmetry or antisymmetry of the scoring function on a relation-by-relation basis, in accordance with the observed data. Our empirical evaluation shows that the proposed method outperforms the original Complex Embeddings and other baseline methods on the FB15k dataset.

Connected through hyperedge HE-1 (induced by source paper "correlated feature selection with extended exclusive group lasso"):
  with members:
    - "a complete analysis of the l_1 p group lasso"

Connected through hyperedge HE-2 (induced by source paper "binarized canonical polyadic decomposition for knowledge graph completion"):
  with members:
    - "a survey on methods and theories of quantized neural networks"
    - "word2bits quantized word vectors"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "on multi relational link prediction with bilinear models"
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "binarized knowledge graph embeddings"):
  with members:
    - "a survey on methods and theories of quantized neural networks"
    - "word2bits quantized word vectors"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "binaryconnect training deep neural networks with binary weights during propagations"

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.LG(Machine Learning)