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: "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
  Abstract: Audience discovery is an important activity at major movie studios. Deep models that use convolutional networks to extract frame-by-frame features of a movie trailer and represent it in a form that is suitable for prediction are now possible thanks to the availability of pre-built feature extractors trained on large image datasets. Using these pre-built feature extractors, we are able to process hundreds of publicly available movie trailers, extract frame-by-frame low level features (e.g., a face, an object, etc) and create video-level representations. We use the video-level representations to train a hybrid Collaborative Filtering model that combines video features with historical movie attendance records. The trained model not only makes accurate attendance and audience prediction for existing movies, but also successfully profiles new movies six to eight months prior to their release.

Connected through hyperedge HE-1 (induced by source paper "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"
    - "beyond human level accuracy computational challenges in deep learning"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "neural collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "bandana using non volatile memory for storing deep learning models"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "convolutional collaborative filter network for video based recommendation systems"):
  with members:
    - "collaborative metric learning recommendation system application to theatrical movie releases"
    - "network in network"

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.IR(Information Retrieval)