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: "moviegraphs towards understanding human centric situations from videos"
  Abstract: There is growing interest in artificial intelligence to build socially intelligent robots. This requires machines to have the ability to "read" people's emotions, motivations, and other factors that affect behavior. Towards this goal, we introduce a novel dataset called MovieGraphs which provides detailed, graph-based annotations of social situations depicted in movie clips. Each graph consists of several types of nodes, to capture who is present in the clip, their emotional and physical attributes, their relationships (i.e., parent/child), and the interactions between them. Most interactions are associated with topics that provide additional details, and reasons that give motivations for actions. In addition, most interactions and many attributes are grounded in the video with time stamps. We provide a thorough analysis of our dataset, showing interesting common-sense correlations between different social aspects of scenes, as well as across scenes over time. We propose a method for querying videos and text with graphs, and show that: 1) our graphs contain rich and sufficient information to summarize and localize each scene; and 2) subgraphs allow us to describe situations at an abstract level and retrieve multiple semantically relevant situations. We also propose methods for interaction understanding via ordering, and reason understanding. MovieGraphs is the first benchmark to focus on inferred properties of human-centric situations, and opens up an exciting avenue towards socially-intelligent AI agents.

Connected through hyperedge HE-1 (induced by source paper "learning diverse fashion collocation by neural graph filtering"):
  with members:
    - "context aware visual compatibility prediction"
    - "learning type aware embeddings for fashion compatibility"
    - "person re identification with deep similarity guided graph neural network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "learning video representations using contrastive bidirectional transformer"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "lxmert learning cross modality encoder representations from transformers"
    - "contrastive multiview coding"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "data efficient image recognition with contrastive predictive coding"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-3 (induced by source paper "an empirical study on leveraging scene graphs for visual question answering"):
  with members:
    - "rethinking visual relationships for high level image understanding"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "vrr vg refocusing visually relevant relationships"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"

Connected through hyperedge HE-5 (induced by source paper "m vad names a dataset for video captioning with naming"):
  with members:
    - "end to end face detection and cast grouping in movies using erd h o s r e nyi clustering"
    - "generating descriptions with grounded and co referenced people"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "dense captioning events in videos"
    - "learning from video and text via large scale discriminative clustering"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "a graph based framework to bridge movies and synopses"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "person search in videos with one portrait through visual and temporal links"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "from trailers to storylines an efficient way to learn from movies"
    - "multilevel language and vision integration for text to clip retrieval"
    - "unifying identification and context learning for person recognition"
    - "holistic multi modal memory network for movie question answering"
    - "dual encoding for zero example video retrieval"

Connected through hyperedge HE-7 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "centroid based deep metric learning for speaker recognition"
    - "self supervised learning of face representations for video face clustering"
    - "learnable pins cross modal embeddings for person identity"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"

Connected through hyperedge HE-8 (induced by source paper "movie plot analysis via turning point identification"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "universal sentence encoder"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "adaptive document retrieval for deep question answering"
    - "whodunnit crime drama as a case for natural language understanding"

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.CV(Computer Vision and Pattern Recognition)