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: "world knowledge as indirect supervision for document clustering"
  Abstract: One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.

Connected through hyperedge HE-1 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "elixa a modular and flexible absa platform"

Connected through hyperedge HE-2 (induced by source paper "relation discovery with out of relation knowledge base as supervision"):
  with members:
    - "variational knowledge graph reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "incorporating relation paths in neural relation extraction"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "complex embeddings for simple link prediction"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-3 (induced by source paper "time sync video tag extraction using semantic association graph"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "video captioning with guidance of multimodal latent topics"
    - "permanence and community structure in complex networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "meta path constrained random walk inference for large scale heterogeneous information networks"):
  with members:
    - "language models with transformers"
    - "axiomatic ranking of network role similarity"

Connected through hyperedge HE-5 (induced by source paper "machine learning with world knowledge the position and survey"):
  with members:
    - "solving hard coreference problems"
    - "a neural knowledge language model"
    - "harnessing deep neural networks with logic rules"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "cross lingual dataless classification for languages with small wikipedia presence"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "a restricted visual turing test for deep scene and event understanding"
    - "a survey of heterogeneous information network analysis"

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)